in popel the what to say component determines the content to be generated and gradually carries it over to the how to say component which formulates a sentence incrementally
although our model ca n NUM roduce filler terms and repair prior utterances our chief concern is the tine structure of spoken discourse which is closely related to incremental utterance pro tnction
then the correlations between the co occurrence features of the words are calculated pairwisely with tile assistance of a basic word bilingual dictionary
NUM NUM often causes omission a nominal compound is not extracted and noise an inappropriate word string is extracted
the japanese text processing is composed of sentence segmentation morphological analysis and co occurrence data extraction
the linguistic approach primarily extracts correspondences between compound words by consulting a bilingual dictionary of simple words
suppose that compound word w is composed of lwo simple words w and w
likewise the english text processing extracts a co occurrence set for each word from an engish text
still the method is effective because it can extract from a small corpus
however they have the potential of increasing the reliablilty of the correlation values
examples of word correspondences extracted from a patent document are shown in table NUM
the comparison of results before and after feedback supported the effectiveness of using feedback
both results before and after feedback were obtained to evaluate the effect of feedback
like common nouns proper nouns exhibit systematic metonymy united states refers either to a geographical area or to the political body which governs this area wall street journal refers to the printed object its content and the commercial entity that produces it
for a human name these components include a professional title e.g. attorney general a personal title e.g. dr a first name middle name nickname last name and suffix e.g. jr
more expensive models which use full syntactic parsing discourse models inference and reasoning require computational and human resources that may not always be available as when massive amounts of text have to be rapidly processed on a regular basis
also listed are exception words such as upper case lexical items that are unlikely to be single word proper names e.g. very i or tv and lower case lexical items e.g. and and van that can be parts of proper names
indeed we have observed that if several unambiguous variants do co occur as in documents that mention both the owner of a company and the company named after the owner the editors refrain from using a variant that is ambiguous with respect to both
the splitting process applies a set of heuristics based on patterns of capitalization lexical features and the relative scope of operators see below to name sequences containing these operators to determine whether or not they should be split into smaller names
even where name databases exist text needs to be scanned for new names that are formed when entities such as countries or commercial companies are created or for unknown names which become important when the entities they refer to become topical
alternatively it may be independent of the preceding np as in np carnegie hall pp for np irwin berlin where for separates two distinct names carnegie hall and irwin berlin
similar structural ambiguity exists with respect to the possessive pronoun which may indicate a relationship between two names e.g. israel s shimon peres or may constitute a component of a single name e.g. donoghue s money fund report
each basic lexical entry is a disjunct le in an implicative constraint on simple word
the compiler ensures the automatic transfer of properties not changed by a lexical rule
note that the lexical rules NUM and NUM remain applicable in q14 and q16
in fact one can view the representations as notational variants of one another
unfolding the frame predicates for the example entry with respect to the interaction predicate
a local region of a description is either of the following a non4erminal and the child non terminals that it immediately dominates a non terminal which dominates a terminal symbol position along with the terminal and the input segment if present filling the terminal position
the cell mf a c may contain an ordered pair of subtrees the first with root m covering input a b and the second with root f covering input b l c
example NUM the set of cell categories s f y m p mf the last category in example NUM mf comes from the rule y mfm of example NUM which has more than two non terminals on the right hand side
in chart parsing the set of cell categories is precisely the set of non terminals in the grammar and thus a cell contains a subtree with a root non terminal corresponding to the cell category and with leaves that constitute precisely the input substring covered by the cell
this property requires that for any substring of the input for which partial descriptions are constructed the set of possible partial descriptions for that substring may be partitioned into a finite set of classes such that the consequences in terms of constraint violations for the addition of structure to a partial description may he determined entirely by the identity of the class to which that partial description belongs
m v do not parse v into a margin position p c do not parse c into a peak position parse input segments must be parsed fill m a margin position must be filled fill p a peak position must be filled the first two constraints are structurm and mandate that v not be parsed into a margin position and that c not be parsed into a peak position
one of these kinds of operations fills the cell for a category by combining cell entries for two factor categories in order so that the substrings covered by each of them combine concatenatively with no overlap to form the input substring covered by the cell being filled
for example the verb move is both a manner of motion verb and verb of psychological state
this is formulated by assuming that s belongs to some type which s knows but r does not know at first
so the current discussion does not depend on whether the success of communication is defined by cs cr or common belief
now we want to formulate the notion of meaning game to capture nonnatural meaning in the restricted sense discussed in section NUM
it would be sensible to assume that ux cs m er NUM holds only if es cn
for instance the notion of grammaticality may well be attributed to the computational difficulty in convergence to a common game theoretic equilibrium
cb u is the entity which the discourse is most centrally concerned with at u centering theory stipulates the following rule
backward looking center cb ui of utterance ui is the highest ranked element of cf ui NUM that is realized in ui
in communication games common knowledge on which message s has sent should help the players converge on common belief on the game
third the best payoff is obtained when the players have common knowledge about the game if their utility functions are equal
to illustrate the flavor of these constraints and how they might be regularizable we consider two constraints corr and match named for their similarity to orgun s constraints
we would like to thank dan jurafsky orhan orgun sharon inkelas nelson morgan su lin wu and three anonymous acl reviewers for comments suggestions and support
a t is written for any word w morphological material that precedes prefix p material and a NUM is written for any other segment
the corr constraint requires that for every element in the surface phoneme string there is a segment in the underlying word or prefix and vice versa
we envision that these heuristics will be based on the harmony mark scoring of the fst but the exact nature of this is left to future work
in two level ot g n generates all strings faithfulness constraints in eval minimize the inserted and deleted material between underlying and candidate surface forms
first row of table NUM shows the best results obtained by relaxation when using only binary constraints b
in the example fig NUM cl t12 is the most likely pair of class cl and c2 t23 the most likely pair of class e2 aname given by the author
a transducer completed in this way disambiguates all subsequences known to the principal incomplete s type model exactly as the underlying hmm does and all other subsequences as the auxiliary n type model does
increasing the size of the training corpus and the frequency limit i.e. the number of times that a subsequence must at least occur in the training corpus in order to be selected sec
if such units can be identified a priori their translations can be estimated without modifying the word to word model
class conflict errors resulted from our model s refusal to link content words with function words
this paper deals with the discovery representation and use of lexical rules lrs during large scale semi automatic computational lexicon acquisition
the results support two main conclusions
tagged dependency tagged adjacency o i i
left branching is favored by a factor of two as described in the previous section but no estimates for the category probabilities are used these being meaningless for the lexical association method
for the dependency model the ratio is
the accuracy results are shown in figure NUM
the experiments above demonstrate a number of important points
accuracy and guess rates are shown in figure NUM
the equations above assume these probabilities are uniformly constant
five training schemes have been applied with these extensions
as a resuit amount of the fl ature structures unifie l at NUM arsing time is reduce d
phase NUM enumerate possible parses or edges in a chart only with unifiability checking in a bottom up chart parsing like manner
the goals are instantiated by the steps fi om the first one to i th one through structure sharings
ch and e denote a head daughter edge and a non head daughter edge respectively
the other is subsumption a rchitecture where ea ch a gent a cts ra ther independently
this pa per descril es mull i modm method a design nlethod for buihling gra nnna r i a sed
extt acc a inputs alsd colnbine them in sonle sin e ltla slller
received can be critical tha t is the vent stream must be aua lyzed
economy l oes the exl ression save a tlse r s la bor
lllellll item a t t he lllelltps ill a rea a lld lille ill
the architecture of the pie system is shown in figure NUM
the patter n in NUM consists of four components
the assertions are accepted in the decreasing order of their weights
we feel that we have achieved the following objectives in muc NUM
all vacancyjteason slots are filled with the default value reassignment
hollywood was not recognized as a location
quit and hire are obvious omissions
d john smith president and ceo of xyz inc
the errors in coreference recognition caused NUM incorrect descriptors in te
the definitions of these abbreviations are stored in a separate file
on the otller hand ill order to use this method we have to prepa re the amount of knowledge being large euough to cope with various type of described objects
in l his pat er we proposed a scheme which closely depends not on domain knowledge of objects described in manual but on pragmatic constraints which linguistic expressions innately have
this type of method which uses a large lnollut of domain knowledge seems to be dominant froni the viewpoint of disambiguation
the hearer the u er here to is a japanese conjunctive particle which represents a causal relation
in the rest of this paper we will show several pragmatic constraints which can aceonllt for the in terpretations of these sentences described above
therefore the subject of the matriz clause should be a machine if the verb of the matrix clause does not have the non volitional use
in the analysis of the description of an action it is important to examine whether the verb phrase expresses a volitional action or not
shows that the antecedent of the senl e nce is an ssumpi i0n and tire consequence holds on that ssmnption
NUM he distribution of usage of to and reba and that of tara and nara are complementary to each other
we examined a uumber of sentences appearing in japanese manuals according to the classillcation based on the types of agent and the types of verb phrase
finally the extracted lower side is again converted into NUM level format NUM
inform the dialogue partners of important non normal chpsracteristics which they should take into account in order to behave cooperatively in diak gue
the model was represented as a graph structure with system phrases in the nodes and expected contents of user answers along the edges
each problem was considered a case in which the system in addressing the user iiad violated a principle of cooperative dialogue
to this end we developed a set of general principles to be observed in the design of cooperative spoken human machine dialogue
s u interested in discount red out departure time at NUM NUM s no departure at NUM NUM
combining messages according to the highest ranking attribute ensures that minimum text will be generated for these messages
aggregating different pieces of similar information is necessary to generate concise and easy to understand reports in technical domains
this paper presents a general algorithm that combines similar messages in order to generate one or more coherent sentences for them
the author thanks prof kathleen mckeown and dr karen kukich at bellcore for their advice and support
as a result aggregation in plandoc is carried out at the content planning level using semantic fds
the system first ranks the attributes to determine which are most similar across messages with the same action
NUM merging same attribute combining adjacent messages that only have one distinct attribute
generating marker words to indicate relationships in conjoined structures such as respectively is another short term goal
a new fd e2 s1 NUM d1 is assembled to replace those two messages
this considerably simplifies the parsing process
whether zbj precedes or follows hj
table NUM shows the results of the tests
two strategies are employed to improve parsing efficiency
table NUM percentage of dependencies vs number of
NUM NUM the mapping from trees to sets of dependencies
a new statistical parser based on bigram lexical dependencies
finally the model makes no account of valency
we have taken a simpler approach namely
this captures our intuition that an inflection like n in german in itself bears practically no information and is functional only because we normally have expectations and can greatly reduce the range of inflections possible in a given context
we have presented a technique based on the operation of generalization that provides for the automatic computation of a linking relation for use with complex feature based grammar formalisms of the kind that are currently favored in computational linguistics
whenever a lexical entry is found that assigns a category c to a given word form the linking relation is used to determine whether c can be useful in reaching the goal category c i.e. whether c is a transitive left corner of c this test is carried out before the parser looks for rules having c as the left corner
indeed since the grammar allows verbs to subcategorize for any finite number of complements we need an infinite number of links between vp NUM and v n categories
we thus expressly allow reentrancies between the distinct feature structures x and x as we shall see below this is essential in order for us to use the linking relation to instantiate information during parsing
since the information that becomes shared via l NUM is subsumed by that of the reflexive case completion works correctly for left recursive categories but b and b must still be unified in the actual completion step
even without the linking relation unification together with backtracking through a space of possible analyses or corresponding use of a chart gives us information for the missing entry this in itself is not novel
the chain found between this concept and the predicate s head concept only brings forward internmdiate concepts and relations which are aetualised in th presence of the i re ticate and lead to a particular representation of their lnt aning
for example the query about mitsubishi has gained about NUM in precision over smart baseline from NUM to NUM
using more will always retrieve num wanted unless there are insufficient documents remaining that are relevant to the initial vector query
as a result of matching a rule against the input a series of variables within the rule are bound to lexical elements in text
however there still are several places where the choices can be further reduced
the automata resulting from word class specialization group the lexical entries into natural classes
we can summarize briefly alep provides all modules and tools from text handling to discourse processing the latter not introduced here
subact informarion is structured slightly differently as in hpsg to allow for one innovation wrt hpsg which is our treatment of functional elements
application oriented grammar development has to take into account the following parameters efficiency a major problem of ugs is lacking efficiency
for the ls gram project this led to the decision to use a so called lean fornrmism alep providing efficient term unification
the powerful mechanism comes in by the subcatfeature of the functor which allows for a very detailed specification of the information to be projected
for the tags assigned r o the roughly NUM words in these NUM sentences expected error rate was NUM NUM
we then determined the number of duplicate sentence pairs that were exact matches in terms of the way they were parsed and tagged
the results deteriorate rapidly for longer sentences but we believe the problem lies in the search procedure rather than the models
section NUM shows from a formal standpoint how prediction is carried out and more generally how the parser operates
working from the bottom up left to right constrains the parser to produce a unique derivation for each parse state
for instance very short sentences in our trsjulng data tend to be free standing noun phrases or other non sententiai units
a direct model of the conditional probability of the ati parse given the source treebank parse p aif uses only data in the parallel corpus
probabilistic decision trees are utilized as a means of prediction and a grammar with about NUM semantic and syntactic tags and NUM non terminal node labels suppl es detailed lingzdstic information
we have also implemented a generalized version that accepts arbitrary grammars not restricted to normal form with two motivations
we have also confirmed empirically that our models would not be feasible under general permutations
this algorithm can be used in place of the expensive slow best first search strategies in current statistical translation architectures
figure NUM shows that for the case of four subconstituents btgs permit NUM out of the NUM possible alignments
itgs avoid this with their restriction to inversions rather than permutations and btgs further minimize the grammar size
in the earlier system translation of single sentences required on the order of hours sun sparc NUM workstations
we have implemented a version that accepts itgs rather than btgs and plan to experiment with more heavily structured models
due to the unsupervised training the translation lexicon contains noise and is only at about NUM percent weighted precision
however i now can certainty s say will provide for us attain various dominant goal necessary s current expenditure
a representing the semantics of the noun phrase museums in pittsburgh and an underspecified feature structure b representing objects that are compatible with a
the rule is repeated until the feature dst carries a value or the user enters information that causes the unification to fail well typed unification
the desired behavior of the system is to enumerate the prices if the description refers to few e.g. three objects or to display a price range if the description refers to many objects
variables are indexed with the level of representation as in obj o moreover parts of the feature structures can be accessed by specifying a feature path such as
every time a feature structure is added to one of the four levels the appropriate database procedures if any provided by the database blackboards are executed to complete the feature structure
if the source of the path is specified the index of the source intersection is calculated using rules similar to those calculating the destination index not shown in this example for brevity
because of standard english language naming conventions mr jordan is grouped with robert jordan
the precision rate was NUM for the NUM names nominator identified NUM NUM
the next section describes the different types of proper name ambiguities we have observed
aba is grouped with american bar association as a possible abbreviation of the longer name
because of the strong scope of museum as mentioned above no splitting occurs
nominator s first step is to build a list of candidate names for a document
a reliable database provides both accuracy and efficiency if fast look up methods are incorporated
null we have defined a set of obligatory and optional components for each entity type
note for example the recent popularity of software names which include exclamation points as part of the name
to represent an existential that is not scoped by a universal we use the individual ra nk
his is done using an extensional synonym event to connect the concet ts
l oi rel rieva NUM lhe type of lhe chisler must i o id0ntitiod
the integration in a text generation system of such evaluations of instruction specificity level is not a straightforward issue
s displays path to and icon of the museum in our next example we consider an information system in which the user can query prices and characteristics of items place orders and obtain a bill for the the ordered items
in this paper we argue that higher order unification hou provides a linguistically adequate tool for modeling the semantics of focus
in this paper we assume a simplifed version of jackendoff s definition a focus is the semantic value of a prosoditally prominent element
it is commonly agreed that focus triggers the formation of an additional semantic value which we will call the focus semantic value fsv
we begin by a brief summary of l ooth s and krifka s theories and stress the properties relevant for the present discussion
this class intuitively allowing only nesting flmctions as arguments up to depth two covers all of our examples in this paper
hence although rooth s approach captures some cases of soes it does not seem to provide an adequate characterisation of the phenomena at hand
NUM the green more turquoise actually bicycle NUM the bicycle is a joy to ride
NUM s vi ing np a unique rule pattern whose mother is not strictly speaking a grammatical sentence i NUM
if the exceptional cases are ignored it is relatively straightforward to postulate some generalisations about the use of the wu ious punctuation marks
thus their mother category can be either np or s descriptive c ttegories rather than the active vl or locative l p
after partitioning colnplex sentences into inultiple sinli le sentences it searches tile mtecedents in the previous four simple sentences instead of only a previous sentence
then the results of this prel rocessing m e inanually orre t d
how many sentences from the current seiltence do we find tim antecedent of a zero i ronoun in real discourses
how does tile accuracy of tile zero pronoun resolution change if we vary the range of simple sentences where the antecedent of a zero pronoun is searched
the first investigation is l ertormed manually and the result shows that NUM NUM sentences of the review articles from the newspaper consist of NUM NUM simple sentences
these types of search error are non existent for exhaustive search but become important for sentences between NUM and NUM words in length and dominate the results for longer sentences
figure NUM shows the discourse generated when the problem of inoving frolil the mus ksliino center to the atsugi center was given
we assume that plan r18 is obtained when this model finishes uttering moyori no eki made in utterance ul
this global maximum can be found by standard hill climbing methods as long as the step size is large enough to avoid getting stuck on the pebbles
note that the creation of the seed can be delayed until run time i.e. the grammar does not need to be recompiled for every possible query
e.g. the fact that the vp literal in rule NUM is always called with a one element list is ignored here but see section NUM NUM
NUM np p0 p npsem magic np npsem pn p0 p npsem
the explicit definite clause characterization of filtering resulting from magic compilation allows processor independent and logically clean optimizations of dynamic bottom up processing with respect to goal directedness
ail t amax if i is to the primer of thisword
the study of its linguistic abilities offers a persuasive view of its structural power
this approach suits perfectly to spontaneous speech which rarely involves a subject inversion
of a value by local inheritance
off line compilation of logic grammars using magic allows an incorporation of filtering into the logic underlying the grammar
it is possible to make the process more efficient through excluding specific lexical entries with a semantic filter
i i magic s finite ssem magic sentence decl ssem
NUM create a new predicate magic p for each predicate p in p the arity is that of p
to illustrate the algorithm i zoom in on the application of the above algorithm to one particular grammar rule
e2 s1d1 el s3 d2 e2 s1d1 e2 NUM d1 e2 s1d1 e2 s2 d1 el NUM d2 e2 NUM d1 el NUM d2 e3 NUM d2 e3 NUM d2 e3 NUM d2 by site by equipment by date
instead of generating two separate sentences for e2 s1 NUM d1 and el NUM d2 the system realizes that both the subject and verb are the same thus it uses deletion on identity to generate this refinement activated dlc for csas NUM and NUM in NUM q1 and this refinement activated all dlc for csa NUM in NUM q3
our evaluation was as follows NUM unrelevant elements such as punctuation are eliminated fl om both the treebank tree and the parse tree
we calculate these quantities by a suming a binomial distribution for the real test and making sure that the corrected values for yy and nn become equal to their expected value
tninh treebank non inherited the nmnber of bracket pairs in the treebank that were reproduced by the parsing system but were not counted for tinh
we compared two versions of a grammar based parsing system developed at our laboratory using a stochastical grammar to select one parse for every sentence
we also show that statistical significance can not be calculated in a straightforward way and suggest a calculation method for the case of bracket recall
pninii parse error non inherited the number of bracket pairs produced by the parsing system that constitute a crossing error but were not counted for pinh
this is particularly important when developing a parsing system by trying various modifications and choosing the one that performs the best on a test set
these methods only could become popular through evaluation methods for parsing systems such as bracket accuracy bracket recall sentence accuracy and viterbi score
also we have tested two rather similar parsing systems often giving the same answer after all that is often just what one is interested in because one wants to measnre improvement
also a treebank usually contains arbitrary choices besides errors made by humans in cases where it was not clear what brackets correctly reflect the syntactical structure of the sentence
for example according to figure NUM distemper was found as a noun NUM times but many of these uses actually referred to the disease rather than the paint
this means that we do not have to stipulate a separate blocking principle in interpretation since the blocked senses will not be attested or will have a very low frequency
initially we used the automatically assigned part of speech tags to identify verbs but these gave a large number of false positives because of errors in the tagging process
for example we examined verbs derived from several classes of noun from the NUM million word written portion of the british national corpus using the wordlists compiled by adam kilgarriff
where lrl lrn are the n lexical rules needed to derive the n unattested entries for word form j this will yield revised ratios for each given word which can then be normalized to probabilities
the two hidden parameters are the probabilities of the model generating true and false positives in the data
for this we view the parses of the tokens making up a sentence as making up a acyclic a finite state recognizer with the states marking word boundaries and the ambiguous interpretations of the tokens as the state transitions between states the rightmost node denoting the final state as depicted in figure NUM for a sentence with NUM tokens
the feature names are as follows cat major category type minor category r00t main root form agr number and person agreement p0ss possessive agreement case surface case conv conversion to the category following with a certain suffix indicated by the argument after that taml tense aspect mood marker NUM sense verbal polarity
lit itemization the simplest mechanism of rule triggering is to include in each lexicon entry an explicit list of applicable rules
this approach avoids any inappropriate application of the rules overgeneration though at the expense of tedious work at lexicon acquisition time
one drawback of this strategy is that if a new lr is added each lexical entry needs to be revisited and possibly updated
about NUM of all the able adjectives are like readable they mean basically something that can be read
the rules helped acquire an average of NUM candidate new entries per verb sense thus producing a total of NUM NUM candidate entries
during the review process the lexicographer can accept the generated form reject it as inappropriate or make minor modifications
what we do know is that when used justifiably and maintained at a large scope they facilitate tremendously the costly but unavoidable process of semi automatic lexical acquisition
since the root of lhs ra i.e. node n35 belongs to state m27 r3 is applied to c2 at node m27 yielding c3
given that the gemeral structure of instructional texts is hierarchical we chose a representation that e xpresses a hierart hy of goals and sub goals
this tool allows one to define the windows dialog boxes and other widgets relevant for the application under develot ment and produces a prototype of the interface thus specified
they may start from the main goal and work down tile structure or they may start by specifying all the low level actions and object and work up the structure
as an example consider the rei resentatitm of a sub set of the procedure for retying a new file in a microsoft wom like editor shown in figm e NUM
the precondition open save as must be tmrformed before the sub steps may t e attempted and is in turn linke t to fllrther sub plans choosing plan and clicking plan
this mechanism allows authors to drag actions from the actions pane and drop them on the various procedural relation slots in the workspace pane or alternatively to create new actions to fill the slots
indeed we found that a number of the actions and objects in the model could be automatically acquired from a design tool thus providing basic building blocks from which the flfll model could be constructed
a prototype of this system has been implemented correctly tagging NUM of polysemous word tokens in a small test set providing evidence that our hypothesis is correct
an economical way of organizing translation patterns is to include non lexicalized patterns as default translation rules
a pattern for a larger domain of locality tends to give a shorter derivation se null quence
finally in phase NUM the target charts are built to generate translations based on the selected patterns
rather we can view them together as a uniform set of translation pairs with varying degrees of lexicalization
hence we assume without loss of generality that no head constraint is given in pretetminal rules
there is a significant difference between that case what we have called coincidental overlap and the case of two people talking about the same topic to one another
while the expressivity hierarchy is useful for differentiating classes of lmlguages in precise terms like worst case recognition complexity it is easy to use the hierarchy incorrectly
next a software layer defines the functions of ice
ing a channel which is depicted by a dashed line
thus another component c is contigurcd capable of transforming the data
a a ils ilfforlnation service channels can be established by any component
by doing so the search space of the speech recognizer is restricted
these software layers suffice to communicate basic data types like nmnbers and strings
the low level communication routines are provided by pvm see above
the visualization is performed by two additional components la belled ui a and ui b
it is spliced into the h ta path between a and b
only the speech recording module aim some speech recognizers
in the work described here we investigate how far we can get by focusing our attention only on discourse markers and lexicogrammatical constructs that can be detected by a shallow analysis of natural language texts
NUM the position of the marker in the textual unit to which it belonged beginning medial or end NUM the right boundary of the textual unit associated with the marker
another way is to evaluate the impact that the discourse trees that we derive automatically have on the accuracy of other natural language processing tasks such as anaphora resolution intention recognition or text summarization
in order to make rhetorical parsing work we improved previous algorithms for cue phrase disambiguation and proposed new algorithms for determining the elementary textual units and for computing the valid discourse trees of a text
in order to measure this agreement we associated an importance score to each textual unit in a tree and computed the spearman correlation coefficients between the importance scores derived from the discourse trees built by each analyst
the algorithm examines a text sentence by sentence and determines a set of potential discourse markers that occur in each sentence it then applies left to fight the procedures that are associated with each potential marker
the articles are ranked for their degree of relevance to a query in two ways on a scale of one to ten and comparatively by the degree of relevance of an article against all other articles
a procedure that can be used by a shallow ana null lyzer to hypothesize the sizes of the textual units that the cue phrase relates and the rhetorical relations that may hold between these units
in this paper we describe an item familiarity account of the semi productivity of morphological and lexical rules and illustrate how it can be applied to practical issues which arise when building large scale lexical knowledge bases which utilize lexical rules
d punctual actions have no transitional phase between start and end point having a greater effect the basic hypothesis of this study is that the degree of transitivity associated with a clause indicates the level of importance of a clause in a narrative text
a quick response was thus recorded as encouragement to continue the current type of generation and a long response acted to encourage the system to experiment with other values for the constants and seek a new optimum
these include subdialogue behaviors variable initiative the ability to account for a user model the use of expectation for error correction purposes and the ability to handle multimedia input and output
the error correction mechanism looks for an expected input that has a low hamming distance weighted from the actual recognized input and chooses the best match to the input that it will respond to
each of these is described in the following paragraphs
while constructing this system we often wanted to add modules to explore new ideas an animated face the machine learning of the output mode preferences a novel dialogue control algorithm etc
the following sections describe our method
figure NUM adapting to the user s preferences
however due to left recursion in productions the same state may appear several times on a path and each occurrence is counted toward the total i
definition NUM the following quantities are defined relative to a scfg g a nonterminal x and a string x over the alphabet y of g
another advantage is that our probabilistic earley parser has been extended to take advantage of partially bracketed input and to return partial parses on ungrammatical input
then it must be the case that NUM NUM c c i.e. all productions involved are unit productions
in both c and NUM columns the separates old factors from new ones as per equations NUM NUM and NUM
thanks are due dan jurafsky and steve omohundro for extensive discussions on the topics in this paper and fernando pereira for helpful advice and pointers
given an input triple predicate grammatical relation predicate p1 gr p NUM the semantic analyser first replaces the two predicates with their semantic entries two conceptual graphs
gr preferences NUM most specific nodel pair i.e. the use of most specific knowledge associated with words is prefered NUM simplest chain production method see NUM NUM NUM
if one considers that this theme must be a physical object then examples NUM NUM conform to the selectional restrictions of angioplasty while NUM violates them
yve rely for this on t heuristic NUM ath search algorithm that exl loits the gr phic aspects of the eon eptual gratihs formalism
it is difficult to map grammatical relations to static predefined conceptual representations since their meaning in the domain depends on their context of use and mostly on the predicates they link
for instance in our ontology segment artery stenosis and human have four different types and are not comparable by the is a relation e.g.
the selected chain uses the reference model of angioplasty fig NUM and tile definition graph for segment i f fig NUM which are connected on concept trtery qegment
metonymy processing is based on the domain model
a for every concept c of type t ill m1 such that t t2 every path between cl and c in mt is a returned chain
the event NUM NUM is an observing event it represents the assertion of the donkey sentence l h is a defining event used to build the complex concepts i armer1 farmers that own and so beat donkeys and donkey1 donkeys that are owned by these nrmcrs
for exert pie if one tells lolh a i need a hammer one does not want her to answer that she has found a hammer the hammer that you need
no claim is made that the representation reflects the world as it really is if there is such a thing nor even that the representation reflects some consensus view of the way the world is
the degree to whieh such a representation is lis rilmled depends on tim proportiotl o so i ions of the r l rcscnta tion
in this case we call the sequence rl r rcb a resolution sequence
nnctor in prolog which delays the evaluation of the second argument of the functors if the first arguruent is not instantiated
basi ally phase NUM parsing creates these tuples and hase NUM parsing uses them
assmne that the noun phrases my colleague and good paper are already recognized by a parser
pars trees in the naive way described in se tion NUM as the
the selected speech acts are encoded in the grammar in the phoenix case
the model structures dialogue in two levels of finite state machines with the final goal of improving translation quality
this paper proposes a two layered model of dialogue structure for task oriented dialogues that processes contextual information and disambiguates speech acts
let as and n be s s and r s strategies NUM respectively
fred is realized by the subject np and max is realized by the object np
in this setting s should send a costly message to get a large payoff
u1 and NUM are the utility negative 6perhaps there are other semantic contents and messages
an equilibrium is pareto optimal iff no other equilibrium is pareto superior to it
the grammar evaluates the corn putational among others cost of using content message pairs
the notion of equilibria in a meaning game is naturally derived from that in a signaling game
for instance a reference to fred by he will raise the salience of fred
oil severa cases of gra ljmm r based multi modal sys tern developnmnt
this dimension covers the a bseuce or presence of imam NUM lelism at the user int erface
our approach raises tile question which of tile parsers uses what information
they undergo verification at tile semantic level
the percentage vahws are relative to synsem
the general idea is as follows
the grammar cospecifies syntax and semantics in the attributes syn and sem
the responsibility for the content of this study lies with the authors
incrementality and interactivity imply a steady exchange of messages between the parsers
r rule id and l lex id denote rules and lexicon entries resp
this might lead to several problems which we address in section NUM NUM
alter that we will describe the conmmnication protocol between the parsing processes
NUM a jon only likes mary b lyc it is also usumly agreed that certain linguistic elements associate with focus in that the meaning of the utterance containing these elements varies depending on the choice of focus
ti ular it adequately captures the interaction of focus with vp ellipsis as illustrated by kratzer s notorious ex ttni le i ordy wer t to tangle woo NUM because you did
approach the unification problem can be stated as follows given two terms of a logic m and n is there a substitution or of terms for variables that will makethe two terms identical i.e.
NUM a jon only int vduced paul to mary b ion only intr od tced paul to mary to model this association with focus phenomenon the semantics of associating elements e.g.
in short we obtain a reading similar to that of l tooth the difference being in the way the fsv is determinecl by itou in our approach by means of a semantic definition in rooth s
his central notion of restriction whereby a restrictor is a finite subset of the paths specified in a feature structure is related to the technique we introduce here since both guarantee the finiteness of an otherwise possibly infinite domain of complex categories but shieber s restrictors are specified manually
whenever a constituent be it a word form or a phrase is successfully parsed the syntax rules are chosen which have the category of the identified constituent as their left corner i.e. the left most category in the right hand side of the rule
for example the concept clause modifier rankingl t is realized as an adverb clause modifier rankingll as a prepositional phrase and clause modifier embedded as an adverbial clause
this effect is attributed to the utility assignment as shown in figure NUM
another requirement is that this intermediate representation is easy to control since a mathematical text must conform to the syntactic rules of its sublanguage
like speech acts pcas can be defined in terms of the communicative goals they fulfill as well as in terms of their possible verbalizations
the crucial point now is that the first sitspec is fully embedded in the second this is in correspondence with the truth conditions if sally has sprayed the wall with paint then she also has sprayed paint onto the wall
the final goal is to improve translation quality in a speech to speech translation system
the author gratefully acknowledges support from in caixa fellowship program
NUM s1 the double room is NUM a night
a more local history predicts the expected response in any adjacency pair
for example actee actor means replace the term actee in the psemspec of the old verbalization where it was optional with ac eor which is not optional
do you have that information already
null as a step forward to a more fine grained distinction between participants and circumstances we differentiate between requirements of process types as coded in the um and requirements of individual verbs which are to be coded in the lexical entries
in this paper we have argued that semantic tagging can be carried out only relative to the senses in some lexicon and that a machine readable dictionary provides an appropriate set of senses
no NUM is a n examph o1 a useless ol ocalrioit
the sut port functions described in section NUM are traditionally used in relaxation algorithnts it seems better for our purt ose to choose an additive one since the multiplicative flm tions might yiehl zero or tiny values when as in ollr cose for q crtain val iable or tag no constraints are available for a given subsel of vm ial les
we can use the same information than hmm taggers to ot tain automatic onstraints the NUM robability NUM of transition fl om one tag to another bigram or binary constraint probability will give us an idea of how eomt atible they are in the positions i and i NUM rod the same for l rigrain or ternary cbnstraint probabilities
this means that we ha ve a great model flexibility we can choose among a completely hand written model where a linguist has written all l he constraint s a comph tely mm mat ically lierived model or ally interinediate olnl ination of onstrailfl s fl om ea ch ype
the main advantages of relaxation over markovian taggers are the following first of all relaxation can deal with more information constraints of any degree secondly we can decide whether we want to use only automatically acquired constraints only linguist written constraints or any combination of both and third we can tune the model dding or changing constraints or compatibility coefficients
the results he re point to the same conclusions than the use of trigrams il we have a good trigrmn model as in wsj then the back off technique is usefifl and we get here the best overall result for tiffs corlms
to a hieve this we define a new sul port flmc l ion which is the sequence i robability being t k tile tag for varial h vk with highest weight value a the current tilne step
finding such a criterion is point that will require fllrther research of tit supi ort function does n t correspond ea actly to the best solution for the problem that is the chosen flmction is only a n approximation of the desired one
x p m x x p z m which is the product of tile current weights for the labels appearing the constraint except vi t rcb representing how applicable is tile constraint in the current context multiplied by c which is the constraint compatibility value stating how compatible is the pair with the context
currently happemng on the screen the central system components have been derived from observation cooperating agents
rat to of the vertices at each level which a ro ra ndomly soie ted as lea vortices in t tree
it has natural language processing applications in searching for matches in example based translation systems and retrieval from lexical databases containing entries of complex feature structures
this paper addresses the computational problem o retrieving trees that are close to a given query tree in terms of a certain distance metric
the distance between two sequences measures the minimum number of insertions deletions and leaf label changes that are necessary to convert one tree into another
the paper first presents the approximate tree matching problem in an abstract setting and presents an algorithm for approximate associative tree matching
hc ourth column gives the trl xinmirl nltnibet of children that a uon leaps node lna y h tvo
states are then removed from the front of the queue and used to complete other states
data processing explanatmns start of process figure NUM gives a screenshot of the
this is true even when the event has only occurred once as is often the case with linguistic phenomena
the notion of a syntactic head is similar to that used in unification grammars although the heads in our patterns are simply encoded as character strings rather than as complex feature structures
NUM thus our strategy favors lexicalized or head constrained and collocational patterns which is exactly what we are going to achieve with pattern based mt
this work indicates that as a learner is mastering a subject there is a certain subset of the material that is currently within their grasp
clearly this technique is missing something important
the assumptions behind rank correlation are few
so this approach may transpire to be
this is a very important difference
standard deviation shown in each cell
the subdomains are labeled as follows
the similarity metric used in b must be chosen carefully
it is clear that ernail is highly heterogeneous and therefore inherently unpredictable
in the first ebl learning phase a parsed training corpus is used to identify chunks of rules which are combined by the ebl algorithm into single macro rules
multiplication is used here because acoustic and lexicosyntactic likelihoods for a word or constituent would appear to be more nearly fully independent than fully dependent being based on very different kinds of information
given a sufficiently large corpus parsed by the original general grammar it is possible to identify the common combinations of grammar rules and chunk them into macro rules
the judge was a first year undergraduate student with a good knowledge of linguistics but no prior experience with the system the process of judging the corpus took about two and a half person months
the rest of the paper is structured as follows
all the results presented above pertain to english only
parsing proceeds by interleaving constituent creation and deletion
constituent pruning then removes all sufficiently unlikely edges
the first is the popular idea of statistical tagging e.g.
we find in this genre most of the verbm processes entirely absent from procedure
because of their distinct communicative purposes we again feel justified in calling these genres
the emphasis is on paradigmatic choices as opposed to syntagma ic structures
we decided therefore that their material would be unsuitable for our purposes
constraints and preconditions states which must hold before a plan can be employed successfully
results states which arise as planned or unplanned effects of carrying out a plan
genre is responsible for the selection of a text structure in terms of task elements
if the sub steps are not primitive they can themselves be achieved through other plans
ready reference is more weighted than procedure towards dependent clauses and is particularly marked by the presence of temporal conjunctions
broadly speaking they fall into two categories top down methods and bottom up methods
the boundary markers noun verb to and at which are relevant to the pattern x a y create active arcs by combining left neighboring passive arcs
if the retrieved pattern is of the type x a and a left neighboring passive arc can satisfy the condition for x s instantiation create a passive are for x a
in the combination of NUM and NUM y ni x with the distance value NUM NUM is selected as a target expression
to creates the active are NUM whereby the variable x of x to at verb phrase is matched against NUM
similar results which show the llse llhless of the new ti mt tbr spokenjanguage translation were obtained in other tyl es of translation such as jal anese to english or korean translation
we have proposed an increlnental translation method in transfer driven machine lyanslation ti mt in this method constituent boundary patterns are applied to an input it a bottom up and left to right fhshion
in this section we perform fmglish to japanese translation to compare the efficiency of the top down pattern application with that of our new method based on the bottom up application and substructure preference in the tdmt prototype system
in order to limit the combinations of patterns during pattern application we distinguish pattern levels and for each linguistic level we specify the linguistic sublevels which are permitted to be used in the assigned variables
NUM if the processed string is a constituent bound null ary a create each kind of arc as follows according to the pattern i retrieved from the constituent boundary
therefor in parsing goes to chinatown we use the pattern x to y which has two variables x and y and a constituent boundary to
thus right winger right fielder and heavy sleeper are recognized as deriving from right wing right field and heavy sleep short staffed short lived and light industrial are recognized as derived from short staff short life and light industry
as expected on the basis of this semantic attribute right usually means not left when it modifies side in its locational sense flank but virtually always means not wrong when modifying side in its commitment sense
in some cases when a noun or even a noun sense is consistent with more than one sense of the target adjective that modifies it a default target sense may be reliably inferred so long as there is no strong counter evidence in the immediate context
we evaluate the potential of such a procedure by extracting from the co occurrence sentences a set of nouns that are indicators for the senses of the target adjectives and applying them to instances of the targets from non co occurrence sentences in the corpus at large
in the face of such examples it becomes difficult to interpret the adjective in such sentences as but the car now on the right not left side of the road was too late to veer away from the second tire
or to a person having that relationship or role with old having the sense aged he rang to six friends not too young not too old and explained that he d have to postpone their dinner
in fact if it were necessary to specify such entities as being light in color we would expect that their functional specificity for lightness in weight would lead to the use of a more specific qualifier such as light colored rather than simply light
this raises the possibility that the specificity of nouns for target adjective senses might be influenced by the nature of the sentences in which they occur those that contain largely repeated contrastive structures we need evidence concerning their sense specificity from the corpus at large
in order to work with another language the following resources are needed NUM pre tagged training text in the new language using same tags as before NUM a tokenizer for non token languages NUM a pos tagger plus translation of the tags to a standard pos convention and NUM translation of designators and lexical list based features
whether a phrase or word is a proper name and what type of proper name it is company name location name person name date other depends on NUM the internal structure of the phrase and NUM the surrounding context
this can be attributed mainly to i locations are commonly associated with commas which can create ambiguities with delimitation and NUM locations made up a small percentage of all names in the training set which could have resulted in overfitting of the built tree to the training data
to update the old forward inner probabilities a and NUM to cd and NUM respectively the probabilities of all paths expanding y t have to be factored in
in figure NUM and NUM represent the explicit word delimiter and the explicit sentence delimiter respectively
for z y this covers the case of a single step of prediction r y l y NUM always since rl is defined as a reflexive closure
fortunately all repeated prediction steps including those due to left recursion in the productions can be collapsed into a single modified prediction step and the corresponding sums computed in closed form
inner probabilities are thus conditional on the presence of a given nonterminal x with expansion starting at position k unlike the forward probabilities which include the generation history starting with the initial state
thus from a practical point of view word based n grams are preferable in order to further suppress fractional expressions and pointer table use
earley s parser and hence ours also deals with any context free rule format in a seamless way without requiring conversions to chomsky normal form cnf as is often assumed
the probability ex can be seen as the precomputed inner probability of an expansion of x to the empty string i.e. it sums the probabilities of all earley paths that derive c from x
our strategy will be to collapse all predictions and completions due to chains of null productions into the regular prediction and completion steps not unlike the way recursive predictions completions were handled in section NUM NUM
the book will peter bought have can peter will have been able to buy the book
itowever no such problems ofovergeueration or spurious ambiguity arise if one adopts llypothesis b instead
in this paper we have argued on empirical grollnds that subsumption should be the relevant operatiw criterion
lation of the relevant slash value that is introduced via the ceia lcb g in the le for kaufcn
finally we have shown that tile subsumption test for the applicability of lexical rules an be
if the list is nomempty the h ftmost cate gory in the list represents the direct object
den mann in la in its active form corresponds to an np with nominative case e.g.
then words belonging to a list of stop words prepositions pronouns etc are removed
this algorithm assigns a single sense to each token which is the tag assodated with that token
formation provided by each source seems independent of and has no bearing on any of the others
mrds are of course normally generic and much practical wsd work is for sub domains
the text is initially stemmed leaving only morphological roots and split into sentences
graph and NUM of tokens were assigned the correct sense using our simple tagger
we have chosen to use the machine readable version of ldoce as our lexicon
this research was supported by the european union language engineering project ecran number le2110
a proposal to extend this tagger is developed based on other mutually independent sources of lexical information
our intuition here is that disambiguation evidence can be gained by choosing senses which are closest in a thesanral hierarchy
hence c NUM if and only if the sentence can be assigned a synts for an absolutely disconnected sentence c would be equal to the number of words
the main difference is that the parser gets as the input not the initial morphs but the extended one which is constructed by adding new homonyms to the initial morphs
r is succesively assigned values NUM rma x where rma x is set by the user and for each r parsing is repeated from the beginning
its main points are longer fragments are preferred to shorter ones links of earlier stages are preferred to those of later stages shorter links are preferred to longer ones
correctness of links between fragments is determined by grammar rubs which have access to all information about the fragments to be linked including the syntactic entries for homonyms in their nodes
however it can not be established on the syntactic level just as a spelling corrector is helpless if a word is transformed by an error into another existent word
after each stage only maximal fragments are retained a fragment is maximal if its segment is not a proper part of the segment of any other frag inent
in other words r NUM is the least value of r for which the situation becomes unimprovable within the extended morphs constructed for the input sentence
the improvement of the proposed algorithm requires dealing with special cases of anaphors such as cataphors and also with specific cases which are not easily handled in the literature
our approach is to add a special phase that resolves first the prrs occurring in the initial ee before applying the expected focusing algorithm on the same initial ee
this early resolution relies on the fact that the prr pronouns may refer to the agent as in sentence NUM as well as to the complement phrases
the main cases of failure are related to the non implemented aspects like the treatment of coordination ambiguities and the appositions or other anaphoric phenomena like ellipsis
however our algorithm would fail in NUM because the non prr pronoun him could refer to john which occurs in the same ee
the values caught by the filter variables are the arguments of the cs roles i.e. they fill the cs roles
for example we saw that a solution to processing cataphors could be to reconsider the order in which the conceptual structures elementary events beforehand are searched
the main advantages of the proposed algorithm is that it is independent from the knowledge representation language used and the deep understanding approach in which it is integrated
it is clear that taking the sentence in its classical meaning as the unit of processing in the focusing approach is not suitable when sentences are embedded
clearly the ernail corpus is highly heterogeneous
formally if we assume the partial fixed expres
ulen u in ellu i t eln NUM l delete
wrb aetion verbj y menu action
the users had exl erience wit h using cxisti g
objects whi h are not shown on t he display
l oitging at an ot ject select trig
ca t egories thus mlowing for their iutegra tiou
the example illustrates the fact that disambiguation between related senses is not always possible which leads to the further question if a discrete distinction between such senses is desirable at all
the constituent to be topicalized or if not specified in the input the subj is extracte t via the slash mechanism cf
e how about another small slice of pizza
a unified theory of irony and its computational formalization
b thank you for rashing my treasure
b have you seen my pizza on the table
furthermore in the case of 5a after recognizing the utterance to be ironic jesse turns out to know that the speaker peter thinks jesse can not be promoted before peter
example NUM just after his colleague jesse said to him pd be promoted before you peter replied NUM a you d be promoted before ine huh7 b
alludes to the speaker s expectation e NUM includes pragmatic insincerity by intentionally violating one of pragmatic principles and NUM implies the speaker s emotional attitude toward the failure of e
this room may NUM e slightly messy
candy said to her huslmnd NUM a
NUM the speaker s expectation e fails at h
however the basic intuition that awareness plays a role in the choice of surface form is supported as the contingency table for this feature in table NUM shows
consequently only the rules have to be modified if the system should behave differently
note that a span s signature must specify whether its endwords have parents
violating this condition leads to either multiple parents or link cycles
for this reason the models were insensitive to most lexical distinctions
the form and coarseness of such specifications is a parameter of the model
for the resulting sentences to resemble real tort era the
b a cotnmon cr or if the model ignores arity
expression NUM assigns a probability to e very possible tag a nd link annotated
the following example shows the information used for generating two clarification questions to disambiguate the structure shown in figure NUM
we develop here a representation of rule sequences that makes use of dta and that is at the basis of the main result of this paper
it is easy to see that the fact that lhs r has some periodic node is a necessary condition for r to be critical
assume that a critical rule r is to be applied at several matching nodes of a tree c we partition the matching nodes into two sets
NUM can be considered an application o1 gpi infer mativeness and gp9 orderliness as follows
being well structured the ticket reservation task generally lends itself to system directed dialogue in which the user answers questions posed by the system
however the results of pronoun resohliion may not be explicitly r th t d in th out put of t ma hin tral sla tion
by NUM ronouns or definite expressions su h as th is that and tit n the other hint i predi ates were sometimes repeated with different expressions
in contrast when context pro essing is apt lied the focus of also ix determined to i e tom in senten e NUM and orange in sentence NUM
ih solving the focus of fi cusing sul juncts such as also rod only is a tyl ieal context del endent probl m tha t requires ilffornmtion on the NUM revious context
our framework subjuncts e.g. also and only as well as improves the overm1 ae uraey of a natural language for adding sul plemen t try phrases t o seine pro essing system
in this section we describe how the accuracy of senten e mtalysis in other probh nls is improved by referring to the siml le context model and how the results are refiecte l in improved machine translation outlmts
gp6 and ip7 aild gpi and gp9 respectively spell otll the illleilded coilteiits ill two of ihe princilfles
t e fully explicil in et lllnlunicating to tlsel s the coillillitlllents they have illade NUM sp2
the discussion that follows makes the assumption that the right hand side of every production is either a string of non terminals or a single terminal
the results presented here demonstrate that the basic cubic time complexity results for processing context free structures are preserved when optimality theory grammars are used
the treatment of the underparsing operations given above creates the opportunity for the same partial description to be arrived at through several different paths
thus the number of cell categories places a constant bound on the number of passes made through the overparsing operations for a block
a dp table cell which covers only one input segment may be filled by an underparsing operation which marks the input segment as underparsed
a cell of the dp table is filled by comparing the results of several operations each of which try to fill a cell
out of NUM NUM occurrences that overlap about NUM of the sense assignments in our data set agree with those in semcor
these NUM NUM word occurrences consist of NUM nouns and NUM verbs which are the most frequently occurring and most ambiguous words of english
for instance given the sentence a reduction of principal and interest is one way the problem may be solved
they attributed this to insufficient training data in semcom in contrast we adopt a different strategy of collecting the training data set
manual tagging was done by university undergraduates majoring in linguistics and approximately one man year of efforts were expended in tagging our data set
running on an sgi unix workstation lexas can process about NUM examples per second when tested on the interest data set
as such we conducted NUM random trials and in each trial NUM sentences were randomly selected to form the test set
another assignment method is to determine the most frequently occurring sense in the training sentences and to assign this sense to all test sentences
this set of NUM nouns accounts for about NUM of all occurrences of nouns that one expects to encounter in any unrestricted english text
for every word in the two lists up to NUM NUM sentences each containing an occurrence of the word are extracted from the combined corpus
in this example the second part of the first rule will be evaluated in the case of a missing the destination of the path
the rules are evaluated by a central processing unit the general manager that passes control to the agents to evaluate their local operations
NUM do you mean the one at NUM forbes ave the one at sandusky st or the one at NUM forbes ave
null several interesting areas of application come to mind
assuming we have defined classes of segments alv blab and so forth represented as unions of segments we can represent the regular expression for p as in b of table NUM
the sizes of the compiled transducers can be quite large in fact they were sufficiently large that instead of constructing c b beforehand we intersected the NUM individual transducers with the lattice d at runtime
the key assumptions are that the tree predictions specify how to rewrite symbols from an input string and the decision at each tree node is stateable in terms of regular expressions on the input string
thus the entire weighted rule can be written as branch may define expressions of different lengths it is necessary to left pad each with and right pad each p with
while the sizes of the resulting transducers seem at first glance to be unfavorable it is important to bear in mind that size is not the only consideration in deciding upon a particular representation
so for the tree in figure NUM each new novel instance of aa will be handled by exactly one leaf node in the tree depending upon the environment in which the an finds itself
we can derive the and p expressions for the rule at leaf node NUM by intersecting together the expressions for these contexts defined for each branch traversed on the way to the leaf
if e.g. the user entered the museum 2at this time a very restricted language model is generated on the fly
of course using this representation all of the optimal phrase structure grammars a c f and h are identical
the conclusion of the above section might lead us to is that basing phrase structure grammar induction on minimization of entropy is a poor idea
similarly they walked and jumped on ice is grammatical but they walked on and jumped on ice is awkward
each fine shows the filename the title of the text the length of the contingency table and the value for g NUM
a second evaluation method is to integrate the i m with the speech reeogniser and test the combined system using recorded speech data
for comparison therefore a third bnc lm was built using a vocabulary derived directly from the bnc rather than email
enteriug theory rosz etal NUM lcb NUM provides a measure of saliency based on the obserwrtions t hat salient discourse entities are often mentioned rel ea NUM edly
given the rest of the semantic representation for the sentence and the discourse model of the text processed so far the following algorithm determines the focus of the sentence
a thus the cb when it is not dropped is often placed in the sentence initial topic position in turkish regardless of whether it is the subject or the object of the sentence
zalternatively using the canonical sov sentences as the expected frequencies the observed frequencies for the noncanonical osv sentences significantly diverge from the expected frequencies x NUM NUM NUM p NUM NUM
the following sections describe the algorithms used by the sentence plauner to determine the is of the lslrkish sentence given the semantic representation of a parsed english sentence
the intditive reason for this is that speakers want to form a coherent discourse by immediately linking each sentence to the previous ones by placing the cb and discourse old topic in the sentence initial position
NUM if NUM fails if there is a situation setting adverb in the semantic representation i.e. a predicate modifying the main event in representation choose it as the discourse new topic
in such cases the system can take advantage of the fact that type inference can be driven by the combination of the information that is related to two separate strings preposition and verb verb ending and verb stem as is exemplified in our proposal
in fact many putatively horizontal relations may be simply re expressed within a type hierarchy by viewing them dom a compouential perspective obviating the need for expressing them on the orizontal dimension which may lead 6o the use of lexical rules
we adopt the view that verb l redicatcs are open to contextual information which ntttst i e contrasted to the approaches whereby verb predicates are treated am fully formed objects which dictate tit exact nature of their dependents
a w rbal predicate that does not alternate such us the predicate to put NUM NUM is assigued the upppropriate most specific semantics for its synsem iloc icontinuoli us isem ions
this is significant considering the other lms were trained on corpora that were several times larger
an initial subsequence ci starts with the sentence initial position has any number incl
stating that every middle subsequence must begin NUM with the same marked unambiguous class e.g.
by contrast a dynamic lm would adapt to the current input and update its probabilities accordingly
for example some assume a normal distribution which is clearly inappropriate for textual data
however methods such as this can not be justified by subjective judgement and anecdotal evidence
consider the following pair 7e carry out a dry ventilation of the reactor
to deal with such modifiers a minor extension of rules rl and r2 is required
NUM discusses closely related phenomena concerning german english and french instructions
7f ventiler s chement le rdaeteur
a denominal verb covers not only the main predicate but also an argument of the predicate
to ensure such correspondences an additional it llz remove lockwire from filter bowl
we can hardly find an acceptable paraphrase of 5e built on a simple verb
sem nput actlon token i illoc walue imperatlv4
its propositional content is an action of type fill which has two arguments agent and patient
sections NUM and NUM focus on specific types of lexical differences and the related lexicalisation mechanisms
results obtained show that the algorithm not only can equal markovian taggers but also outperform them when given enough constraints or a good enough model
constraints for subsets of two and three variables are automati ally acquired and any other subsets are left to the linguists criterion
the difficulty to choose the support and updating fun tions more suitable for ea h l artitular prol lem
hfitially we will assign to each word il s most i ro able tag so we start optimization in a biassed point
in addition we can question the appropriateness of using probability values to express compatibilities and try to find another set of values that fits better our needs
the conshnint set l lcb elaxation labeling is a bh to deal wil h constraints NUM etween any subset of wn ial les
the algorithm finds a combination of values for a set of variables such that satisfies to the maximum possible degree a set of given constraints
the compatibility value for these constraints is coinputed from their occurrences in the corpus and may be positive compatible or negative incompatible
obviously more needs to be said about the control strategy of the modified interpreter since garden paths and structural ambiguity must be dealt with before new entries are postulated but that goes beyond the scope of this paper
calvil believes that th l l es him the tic will give t ack 2a ualvi n versehe nkt ei n hueh an hobbes
in the subset of roles that are not blocked there are on the one hand roles referring to obligatory actants and on the other hand roles referring to optional actants
on the one hand these results mark directions tbr the developme nt of a comprehensive lexical theory that include s for example an elaborated concept hierarchy with associated axioms
firstly the representatioi format fin the generalized case information is only sketche l an algorithm for case assignment is not given with each verb is associated a liven
the new joined representation format is introduced in section NUM by analyzing the german verbs leihen in its variant to lend and verschenken in its variant to give as a present
additionally there is the lending person s belief in a return of the involved object in other words the belief that the change sign from s o to s is temporary
this belief consists of an inversed iian i si n eveiit c i.e. a return with its resulting disposal onfiguration sa
which roles have emphasis and which do not have emphasis which are the ones that must be verbalized and which are the ones that need not be verbalized is determined according to general rules
some of these parts are then represented by variables that have to be filled in by objects referring to states or vents and other parts deliver relationships between these states or events
levels of tutdt i jnoda NUM i q ut s
pro gramnfing is a step ill the right direction it
it is our contention t3m lcb while evenc ba sed
gui such a s windows a nd ma cintosh
pointing at a different type of object overlapping the recta ugle
NUM mode interpretations shouhl be referred to one another
since level s NUM a nd NUM require tight iutegra tion
it becomes more dimcult to interpret expressions as the level increases
the second case is thal verbs in the nlatrix clauses are in nell volitional use
tara and nara are very rarely used in manual sentences as far as we examined
now we have to deline the term subject we used in this paper
in the rest of this paper we will focns on the zero pronoun resolution
we show that these distributions of usage can be used for resolution of zero subjects
2it seems to be more natural that ce is interl reted as the hearer
the ru form is the basic form of verbs and it denotes the non past tense
therefore tile matrix clause should not express the judgement and attitude of the speaker
NUM NUM subjects of comi h x s mten es with
constraints of this form are not passed on after satisfied once and are not passed out of the local domain
matchmaking dialogue modelling and speech generation meet
however the problem with these systems is that they still start from a given text and are hence restricted to those kinds of discourse information that can be reconstructed from that text
for instance ich durchsuche die datenbank
first the co occurrence infi rmation for each word in both languages is extracted li om the corpus
the relations between a compound word and its constituent words are not strictly speaking co occnrreuce relations
however their relative values are significant when either a japanese or an english word i s fixed
figure NUM shows how two words are associated through their contexts each expressed in its respective language
the intersection of pseudo co occurrence set cp jw and english co occurrence set c ew is then generated
a character string matching routine to identify the correspondences of symbols numerals should thus be added to the correlation calculation module
figure NUM a shows that the number of elements in the intersection exceeds that in the japanese co uccurrence set
in sec NUM we make a remark on the effectiveness of the proposed method and discuss directions for improvement
therefore we exclude the constituent words from the co occurrence set of a compound word and vice versa
j c jw c ew i approximately as illustrated in fig
but now there are NUM class based syntactic signatures as compared with NUM verb based signatures from before
that means NUM NUM of the NUM semantic classes have a complete overlap with a syntactic signature
the levin based verbs are already disambiguated by virtue of their membership in different classes
four of the semantic classes do not have enough syntactic information to distinguish them uniquely
in this case we are interested in the functions that group the verbs syntactically and semantically
table NUM describes the significance of a subset of the syntactic codes in ldoce
in japan for example a dozen or so inexpensive mt tools have recently been put on the market to help pc users understand english text in www home pages
the head of symbol s in the source target rule is identical to the head of symbol v in the source target rule as they are co indexed
tags on the other hand are known to be mildly context sensitive grammars and they can capture a broader range of syntactic dependencies such as cross serial dependencies
the subjects were ninety native japaimse
eigilty dialogues were recorded and transcribed
table NUM distribution for discourse relations
the small ius are hierarchically related
ul musashino sentaa kara wa desune
figure NUM discourse generated by implemented system
fifteen dialogues were randomly chosen for analysis
it is refined in a stepwise manner
t ha l employ a NUM heorem proving reel hod l hat is perhaps implicitly appropriat e for use with linear h gi and combine it wilh labeling system i hat restrict s uhnil t cd deducl ions NUM o be l hose of some weaker logic
v o w o x o y o z we might find that a proof would involve not only an additional assumption corresponding to the positive subformula xo yo z but that reasoning with that assumption would in turn involve a further additional assumption corresponding to its positive subformula z
index sets and t0 denotes union of sets that are required to be disjoint NUM ao b a b b bao 7r a all in proving i a a snccessflfl ow rall analysis is recognized by the prescmee of a database formula indexing in ensuring linear use of resources
observe that the involvement of hypothetical reasoning in this proof i.e. the use of an additional assumption that is later discharged is driven by the presence of the higher order formula and that the additional assumption in fact corresponds to the positive subformula occurrence z within that higher order formula
sidner s focusing mechanism is used as the basic algorithm in a more complete approach
we propose an mgorithm to resolve anaphors tackling mainly the problem of intrasentential antecedents
in the case of rejection it determines which phrase is to move into focus
this work has been supported by the european community grant le1 NUM aventinus project
we propose to consider new kinds of criteria that combine semantic restrictions with sentence structure
a parallel structure to the cf is also set to deal with the agentive pronouns
however the ambiguity will not be huge at this first level of the treatment
first hypothesis ee is the unit of processing in the basic focusing cycle
the anaphora resolution aims therefore at filling the unfilled roles with the corresponding antecedents
NUM mary sacked out in his apartment before sam could kick her out
b propose the dements of c un NUM in the given order
i would like to thank my colleagues in the cps27 group for fruitful discussions
when he gambles i ca n t conceal it
sum of forward probabilities over all scanned states
for all states with terminal a matching input at position i
as an example consider the following simple left recursive scfg
the resulting nonlinear system can be solved by iterative approximation
we will call this procedure spontaneous dot shifting
the second modification is another instance of spontaneous dot shifting
below we relate some of the lessons learned in the process
however the basenp model is needed for two reasons
the most likely parse under the model is then
rol ust l nglish pa rser to lilter out the wrong colloca tions
the head word for each constituent is shown in parentheses
the following is a series of procedures to extract flexible collocations
the parsing algorithm is a simple bottom up chart parser
it is an effective measure for the determination of domain specific terms e.g.
further steps of text analysis e.g.
the network can be minimized and determinized
figure NUM generation of an nl type transducer
the speed of tagging is also improved
p cps NUM ps b clltl
it is also possible to adapt lms dynamically using cache based methods e.g.
it therefore exhibits a level of diversity surpassed perhaps only by spontaneous speech
we have identified and in some cases begun work on the following areas evaluation of semantic analysis with a reversed lexicon that is based on the original analysis lexicon it is possible to take the output semantic representations from the analyser and submit them to a text generator
it also seems to depend on the kind of application involved monolingual generation naultilingual generation machine translation generation of sentences vs texts vs speech or also generation from raw data vs from conceptual representations built with generation in mind
another advantage of reversing an analysis lexicon is using it to regenerate the same text that was parsed to gain some insight into the issue of the pivot point between parsing and generation and as a resnlt of this what is the best input for generation
for instance the ee triggered by enjoy as in i enjoyed the salmon very much must be modeled with a semantic representation so that its recovery can be taken care of as in ii i enjoyed eating
there is no one to one mapping between semantic categories or concepts and lexical items and some events such as interact socially here can be lexicalised as nouns figure NUM NUM or verbs such as in compa i a
sub j sere sun obj nlsem learn sem agent human theme z information figure NUM sense entries for the spanish lexical item adquirir
in order to take into account multiple word senses we followed several paths at the same time
verb object syntactic relation is the weakest knowledge source
no previous work has reported any such evaluation either
the recognition problem for l x i.e. is x an element of ps l is equivalent to the non emptiness of the production set of old
let g be the cf backbone of l we first build g the cfg shared parse forest by any classical general cf parsing algorithm and then l x its liged forest
t l t l l l the sequence of productions rl0 rio rno considered in reverse order is a string in p
the judges were given no instructions about the criteria that they had to apply in order to determine the clause boundaries rather they were supposed to rely on their intuition and preferred definition of clause
iere we summarise the two relations in the form of the following planning statements generates fl iff c is the body of a plan e whose goal is ft
in particular for rhetorical planning of the communication of particular content we can use the preferences observed for selection of the preferred rhetorical relation in the language in question
in this paper we have gone some way towards isolating the specific point in the generation procedure at which pragmatic information such as rhetorical relation must be brought into play
in this case the two orderings of the relation the expression of enablement in french instructions figure NUM is limited to two rhetorical relations sequence and purpose
while portuguese generation is overwhelmingly expressed through the rhetorical relation of purpose in french it is more evenly distributed between purpose and means with a small showing for conditmn
finally just and simply are markers that only appear with the ing clement but there is always another marker that appears in the ed element in conjunction with them
second tile overlap in expressions between ed and ing elements is relatively small it is confined to only two of the five types of expressions infinitives and passives
NUM l br prolonged viewing ttle slide may be pushed downwards and then backwards until it locks under the ledges at each end of the slot
english h s the greatest tolerance of mmmrked discourse relations among the languages studied only NUM of the NUM clauses examined appeared with a marker of any kind
lexas assumes that each word in an input sen null tence has been pre tagged with its correct pos so that the possible senses to consider for a content word w are only those associated with the particular pos of w in the sentence
it is unclear how yarowsky s method will fare on wsd of a common test data set like the one we used nor has his method been tested on a large data set with highly ambiguous words tagged with the refined senses of wordnet
since wo dnet only provides sense definitions for content words i.e. words in the parts of speech pos noun verb adjective and adverb lexas is only concerned with disambiguating the sense of content words
in the output each word occurrence w is tagged with its correct sense according to the context in the form of a sense number i where i corresponds to the i th sense definition of w as given in some dictionary
this task is crucial to lexicon development and maintenance since it provides lexicon developers with the means to check the empirical adequacy of their analyses
because they have a common leading subpath syn we can collapse them into a single statement about syn alone wordl syn verb
the present paper shows that the language is nonetheless sufficiently expressive to represent concisely the structure of lexical information at a variety of levels of linguistic analysis
the resulting value is am and not a as it would have been if the descriptor in declension2 had been local rather than global
so now wordl irrherits from love rather than verb but love inherits from verb so the latter s definitions are still present at word1
the other reason is that one can fall into the trap of using variables to express generalizations that would be better expressed using the path extension mechanism
local inheritance from bare verb to come implicitly defines the following statement in addition to the above come mor past participle mor root
we know of the existence of approximately a dozen different implementations of the language but there may well be others that we do not know of
the definability of the propositional calculus may appear at first sight to be a curiosity one which has no relevance to real life lexical representation
from the tree in figure NUM figure NUM illustrates how each constituent contributes a set of dependency relationships
is there a comma immediately following the first of the hjth word and the jth word
is there a comma immediately preceding the second of the hjth word and the jth word
second it means that words internal to basenps are not included in the co occurrence counts in training data
enote that we count multiple co occurrences in a single sentence e.g. if NUM a b c d c d then c a b c d c c d a b NUM
consider the prepositions to in and of in the following sentence example NUM oil stocks escaped the brunt of friday s selling and several were able to post gains including chevron which rose NUM NUM to NUM NUM NUM in big board composite trading of NUM NUM million shares
for each constituent p c1 cn in the parse tree a simple set of rules NUM identifies which of the children ci is the head child of p for example nn would be identified as the head child of np det jj NUM nn vp would be identified as the head child of np vp
for a constituent to be correct it must span the same set of words ignoring punctuation i.e. all tokens tagged as commas colons or quotes and have the same label ldeg as a constituent in the treebank 1degspatter collapses advp and prt to the same label for comparison we also removed this distinction when the model
similarly the adjective light can refer either to weight or to color in modifying most concrete nouns
this is not a logical requirement both dark and heavy objects are also said to be lifted
thus carry disambiguates light in furniture movers for example carry light objects in their hands
in fact however we can reliably determine the sense of light in a number of these cases
however even with the locational sense of side not wrong is not an anomalous usage
old doctor for example means aged doctor in NUM of NUM instances in our corpus
given this result the set of indicator nouns can be treated as the basis for a disambiguation procedure
the statistical procedure that was used to identify these nouns as indicators is described in detail in the appendix
in contrast the aphb corpus contains no sentence in which both old and young modify house or houses
in contrast there is not one sentence in which both old and new modify either man or men
our learning method is twofold according to the collocation types
basically his methods is based on the longestmatch principle
con bine the two chunks of highest mutual information
first fixed collocations are induced in the following way
dictionary tbr mt 1o find out np orrespondences
these studies c m be classified into two directions
table NUM illustrates the fixed collocations acquired by our method
a sample passage is displayed in tm ie NUM
from previous work we estimate the accuracy of the tagger on the syntactic portion of tags to be about NUM
for each object the system must specify in what world it must be introduced where it is valid and where we can make inferences that bring it into play
so a cohcr mce theory for natural language representation systenls must take into account this fact and limit the c herence vcrilication to parts of texts actually asserted
when a discourse is expressed by mono speaker the assertion f a positive fact the ncgation is not a simple fact provoke a contradiction in the same world
NUM NUM NUM ne gation on fun tire sub objects as indicated previously the uuinber of a ttested arguments of the predicate may change the interpretation of the negation
l r d ed in the t amework of linguistics theory this is the iirst l art of our work not included here duo
the knowledge rcpres ntation rno h is an object ic expross d
note that n extensional object may be an individual or a lass of course all the elements of the class must have the same properties
all the cases treated among others show that a surface negation does not always fit a deep negation and in fact seldom entails n incoherence
this concerns how the propositional content of the current utterance is related to what the conversational participants already know and to the structure of the discourse
present tense whether or not frame l contains an object the semantic role of frame l with respect to frame NUM e.g.
newer statistical approaches with often only very limited context sensitivity seem to have hit a performance ceiling even when trained on very large corpora
going beyond magerman s still relatively rigid set of NUM features we propose a yet richer basically unlimited feature language set
the corpus of NUM sentences that currently have parse action logs associated with them is divided into NUM blocks of NUM sentences each
correct operations ops measures the number of correct operations during a parse that is continuously corrected based on the logged sequence
we present a knowledge and context based system for parsing and translating natural language and evaluate it on sentences from the wall street journal
the parsing of unrestricted text with its enormous lexical and structural ambiguity still poses a great challenge in natural language processing
select noun reading of ncl NUM NUM pl if followed immediately by genitive connector belonging to the set ncl NUM
figure NUM the representation on the semantic level
two level rules have been written mainly for handling morhophonological processes which occur principally in morpheme boundaries
null introduction there are five principal factors in bantu languages which contribute to ambiguous analysis of wordtbrms
if we compare these numbers with those in table NUM we note significant differences and similarities
constrmnt rules are grouped into sections so thai the most obvious cases are disambiguated first
the noun class structure coupled with class agreement marking in dependent constituents contributes significantly to ambiguity
a fairly large part of remaining ambiguity concerns genitive connectors ya and wa and possessive pronouns
because its accurate description in lexicon is not possible alternative ways in handling it are discussed
morphological disambiguation as well as syntactic mapping is carried out with constraint grammar parser cgp
if the system describes details that are already known to the user he or she will become demoralized
after having executed the procedure the agent returns status information and possible return values if any to the general manager which in turn uses this information to decide upon the control strategy
item x z is in our terminology a normalized forward probability i x p p s l x0 i NUM
while earley based on line pruning awaits further study there is reason to believe the earley framework has inherent advantages over strategies based only on bottom up information including so called over the top parsers
appendix a existence of rl and ru in section NUM NUM we defined the probabilistic left corner and unit production matrices rl and ru respectively to collapse recursions in the prediction and completion steps
the recursive instance of the parser is passed any predicted states at that position processes the input up to the matching right parenthesis and hands complete states back to the invoking instance
this increments fl the equivalent of r z y times accounting for the infinity of surroundings in which y can occur if it can be derived through cyclic productions
the worst case complexity for earley s parser is dominated by the completion step which takes o NUM for each input position i being the length of the current prefix
since we can not get an scf of the first order consequences of a possibly inconsistent discourse represented in a more expressive representation language it is necessary to find exactly those logical equivalence preserving conversions which allow us to convert the discourse representation in such a way that the adequate set of consistent information pieces can be made accessible for the disambiguation by locally restricted conversions into scf
since our conceptual knowledge can be represented in a rather restricted representation language it is then possible to show that the restrictions satisfied by the conceptual knowledge and the inferences ensure in an empirically adequate ww the decidability of the problem although a fully expressive language is used to represent discourse
if we also assume NUM for english then a contradiction would result although we did not regard sister as ambiguous at least in our oversimplified language domain ttence if NUM were embedded in a larger discourse we would have no chance to disambiguate other ambiguous lexical items since we would get a contradiction for every reading of these items
we detine tile environment of a type character string group of morl hemes or as tile prol ability distribution of the elements preceding and followdeg ing occurrences of that type in a corpus
for instance although a right context zi ro kate couht match either the postposition ka or the postl osilion kara the longer match kara would always be chosen
NUM for each occurrence of the pos in the corpus iucrement the left vector elenmnt corresponding to the context preceding this occurrence of the pos and increment the right vector element corresponding to the context following the pos
the minimum value of f p will be relatively small when tile environment of the string can be decomposed into a linear summation of some pos environments while it will be relatively large when such a decomposition does not exist
the other experiment used articles fl om one year of the j apanese versiorl of scientific a met ican in order to test whether we could incre lse the accuracy of the morphological analyzer tagger by this method
the aim of this limitation was to reduce computational time during inatehing and it was ell that morl hemes using kanji and katakana characters are too infrequent s contexts to exert much intluence on the results
in general if a string a is a word which belongs to a pos it is expected that the environment d a of the string in a particular corpus will be similar to the environment d pos of that pos
since a word can belong to more than one pos it is expected that the environment of tire string will be similar to the summation across all poss of the environment of each pos multiplied by tile probability that the string occurs ms that pos
the environments for a string and for each pos which it represents become the parameters of the objective flmction defined i y formula NUM and the optimization of this flmction then yields the probabilities that the string belongs to each l os
in fact the grammar model derived from a is as good as any possible model that does not condition n on v
figure NUM extraposition must reduce structural complexity
figure NUM x bar structures of NUM
the word will has two modifiers the head word fits np specifier kinf and the head word of its vp complement bring
there ore ext r3posil m is only allowed when the structural comph xity of l he s hll ellce is reduced 3s a restill
assume that at one step an object derives both a distinguished 3rp and rp0 with the same index p designate associated productions
the textual definitions in each sense is processed to remove stop words and stem remaining words
closeness in such a hierarchy can be effectively expressed as the number of nodes between concepts
NUM how then are we to get an initial lexicon of word senses
NUM wsd using information gained from training on some corpus
we discuss which recent word sense disambignation algorithms are appropriate for sense tagging
in the sentences i have a stake in this country
however these approaches have two major drawbacks first they require hand designed symbolic knowledge like lexica and grammar rules and second this knowledge is too rigid causing problems with ungranlmaticality and other deviations from linguistic rules
performance measure NUM uses an english lr generator handmodeled for NUM years providing results for englishto english translation whereas performance measure NUM uses a german lr generator handmodeled for NUM months hence providing results for englishto german translations
the parser gets the english sentence i have a meeting till twelve the chunker segments the sentence before passing it to the linguistic feature labeler which adds semantic labels see figure NUM
day NUM rego twenty seventh figure NUM chunk parse sentence aligned with its feature structure see text for explanation
in contrast to tim standard feature structure detinition of section NUM an alternative view point is to look at a feature structure as a tree NUM where sets tthis assumes that structure sharing is not possible see section NUM NUM NUM
once having obtained the information in figure NUM producing a feature structure is straight forward using the algorithm of figure NUM sumruing up we can define this procedure as the chunk n label principle of parsing NUM split the incoming sentence into hierarchical chunks
the chunk relation finder then adds relations where appropriate and we get the chunk parse as shown in figure NUM finally processing it by the algorithm in figure NUM gives the final parse the feature structure as shown in figure NUM
this object pj is introduced into the context by pres p2 represents the property of loving u0 s cat where uo is the discourse center defined in the input context
in example NUM we have at ni antecedent xp containing a sloppy pronoun yp and the two controllers tbr yi are smith and jones
the antecedent hisi paycheck introduces a dynamic individual a relation between contexts that introduces i s paycheck to the output context where the value of i is determined by the input context
book o n eoncept is a tangible objectnoun coneept
when these knowledge sources do not permit to identify the unknown forms they are marked as guesses and receive the noun category
a relational database management system rdbms can handle very large amounts of data while guaranteeing flexibility and speed of execution
as soon as even one feature of the complete feature bundle with linguistic information is wrong the analysis as a whole is considered to be incorrect
although the example is trivial the technique is both powerful and useful
this means we now have to establish the value of mor passive participle at word3
from this little description we can derive the following statements inter alia
in practice however this turns out not to be the case
extending these inheritance mechanisms to the more complex datr expressions is straightforward
personal name boolean ends in consonant ends in stop
this behavior is perhaps also more easily introduced procedurally rather than declaratively
there are a number of ways of understanding this global inheritance mechanism
using the mechanisms we have seen so far the answer is no
simple definitional sentences take the form node path def
the marking of these classes extends across the noun phrase whereby the noun governs the choice of markers in dependent constituents
the sequential order of rules within a section does not guarantee that the rules are applied in the order where they appear
e.g. the reading NUM NUM pl gen con is chosen for the analysis of wa on the basis of the ncl of the preceding noun
unlike in languages with right branching word formation where word roots can be grouped together into a root lexicon here word roots have been divided into several sub lexicons
these references can be made either directly to a tag or indirectly through sets which are defined in a special section sets of the rule formalism
although both washiriki and wa are initially ambiguous and in rules the context reference does not extend beyond this pair of words we get the correct result
in the present application also syntactic tags are included into the morphological lexicon as far as the marking can be done unambiguously
swatwol lexicon is at tree where the morphemes of swahili are located so that each route fy om the root lexicon leads to a well formed wordtbrm
ambiguity NUM or the purposes of writing and testing disarnbiguation rules a corpus of about NUM NUM words of prose text was compiled corpus NUM
our results show that in the training corpus of NUM NUM tokens there are NUM NUM words and NUM genotypes
we discovered that in a training corpus of NUM NUM tokens lexical frequencies are not as reliable as genotype frequencies
this paradigm has the advantage of capturing the morphological variation of words combined with the frequency with which they occur
for each genotype we compute the frequency with which each of the tags occurs and we select this decision
they offer a successful solution to the small training corpus problem as well as to the problem of data sparsness
this paradigm has the advantage to capture the morphological variation of words along with the frequency at which they occur
as described in section NUM NUM bigram and trigram genotypes give accurate estimates of the morpho syntactic variations of the language
NUM obtain maximum genotype coverage genotypes must first be separated into closed semi closed and open class
as used in our context the genotype is the set of part of speech tags associated with a word
table NUM shows NUM verb ambiguities NUM noun ambiguities a total of NUM homographs including the adjective form
our principal concern is selection of appropriate intonation
another possibility is to combine it with a scanner for reading printed texts
a verb is assumed to be transitive if its paradigm contains passive forms
no regular experiments were carried out for sentences containing more than one distortion
in a small experiment NUM NUM of the discovered clusters are correct in that none of them contains examples of more than one hand classified senses
sat the next level from the root node it has six classes abstract relations space matter intellect volition and affections
for the evaluation of the results we hand classified the NUM clusters into four groups each of which corresponds to only one sense of kau NUM
the first term of the conditional probability measures the generality of the association while the second term of the mutual information measures the co occurrence of the association
first we assume that for any polysemous japanese verb v j there exists a case marker p which is most effective for sense classification of vj
given tile set eg va the iterations of the association score calculation is o ieg oa t
first the index i and the set of examples eg are initialized as i and eg eg va
also the threshold of deciding a distinction in the sense distribution of japanese case element nouns is predetermined on a fixed level in a japanese thesaurus
therefore the parser described below inds the likeliest legal structure it maximizes the lexical preferences of l within the few hard liuguistic coush ainls itnlrosed by the del endency formalism
firstly in natural language generation a generator should get era re the simphest sentence that conveys the intended meanings
ps1 is the class of languages generated by context sensitive granlmars the sole restriction on production rules in this type of grammar is that the right hand side rhs of each rule is at least as long as the left hand side lhs
in order to recognize the structure of a sentence a parser must establish the dependency links between the words in the sentence
t is well known that the length of a sentence is not a relit ble indicator of its readability
note NUM rcb NUM reduction of structuraj c mtjexity is not l he only const r3int on cxtr3position
for this reason it is useful to distinguish weak and strong containment of a grammar in a language class e.g. a grammar is weakly context free if its stringset is context free a grammar is strongly context free if its treeset is also context free
in constituency grammars that contain the x bar theory as a component dependency relationships between words are implicitly specified in x bar structures
null finally we propose in section NUM that extrapositions re rnotiw ted by reduction of structural complexity
unconditiomd parallel replacement denotes a similar relation where the replacement is not constraint by contexts
context brackets occur only in intermediate relations and are not present in the final resuit
in this section we illustrate the usefulness of the replace operator using a practical example
using the brackets NUM and t to a substring underlined part
other wu iants of the replacement opel ator will be defined later
we also allow a replacement to be constrained by any number of alternative contexts
an unspecitied context is equivalent to the universal sigma star language
for this purpose we introduce auxiliary brackets i after every left context li and i before every right context ri
the inverse replacement maps unambiguously from the lower to the upper side only
in the downward oriented replacement the operation is constrained by the lower left and right context
however in the soe cases the assumption is that the quantification domain of focus operators is identified with the fsv of the source clause
lit is detined on a purely semantic level in the sense that unification operates on semantic representations and relies neither on quantifier raising nor on a rule to rule definition of the fsv
the work reported in this paper was flmded by the l eutsche l orschungsgetneinschaft i fg in sonderforschungsbereieh s1 b NUM project c2 msa
as we have seen above the fsv of la is lb hence by the above semantic for only the semantics of NUM a is
utterances with multiple locus operators NUM are known pathological cases of focus theory NUM a jon only read the letters that NUM arah sent to paul1 b
in the empirical study the human created texts perhaps provided more sufficient information for the hypothetical machine to decide on an appropriate anaphoric form
the numbers in a box except for the first occurrences in the text are the indices of anaphors in the corresponding clauses
the problem is partly because the test texts used in the former comparison are human created while the test texts used here are machine generated
there axe however real difficulties in establishing the significance of the results because of the degree of disagreement among the native speakers
the referring expression component lies between the text planner and the linguistic realisation component in the system as shown in fig NUM
these figures are further supported by the kappa statistic a standard measure of agreement between a set of judges sc88
the task for a speaker to perform was to annotate which form he or she preferred for each anaphor position on the sheets
as an prototypical case consider the following fcp defined for intransitive verbs like to come or to begin
this paper presents a method for automatically recognizing local cohesion between utterances which is one of the discourse structures in task oriented spoken dialogues
on the other hand local cohesion is a bottom up structured context and a coherence relation between utterances such as question response or response confirmation
for example exampie NUM do n t charge or store a tool where the temt erature is below NUM degrees f oz above NUM degrees is coded as unaw because it is unlikely that tile reader will know about this restriction aw is used when it is aware that a is bad
concerning the assumption of independence while it is in fact possible that some of the examples may have been written by a single author the corpus was written by a considerable number of authors
example NUM be careful not to burn the garlic is coded as aw t e ause the reader is well aware that burning things when cooking them is bad
in section NUM we speculated that the hearer s awareness of the choice point or more accurately the writer s view of the bearer s awareness would affect the appropriate form of expression of the preventative expression
it indicates a strong preference ibr the use of the dont form when the reader is presumed to be unaware of the negative consequences of the action to be prevented the reverse being true for the use of the neg tc form
if the negative imperative do not scrub or wet mop the parquet were not included the agent might have chosen to scrub or wet mop because these actions may result in deeper cleaning and because he was unaware of the bad consequences
while clearly the agent will have to intend to perform charging or storing a tool he is likely to overlook at least in s s conception that temperature could have a negative impact on the results of such actions
the possible feature values were dont for the do not and do n t forms discussed above and neg tc for take care make sure ensure be careful be sure be certain expressions with negative arguments
the presented method has obtained a NUM accuracy for closed data and a NUM accuracy for open data in recognizing a pair of utterances with local cohesion
for ambiguity resolution processing of a discourse structure is one of the important processes in natural language processing nlp
the pair of grammatical constructions NUM a NUM b
b a maria falou prep x si p rsprioi obl x
ov see pm torus pm ria torus sees saw ria b mang ida si ria si torus
maria talked about himself to pedro b a maria falou consigo pr6prioi acerca do pedro
this research was supported in part by the praxis xx1 l rogram of the portuguese ministry of science
the alternative solution i propose does not involve different formulations for binding principles or additional principles
only the magic part of the abstract unfolding tree is represented
of course the author is responsible for all remaining errors
this information can then be used to discard the culprit
null relation between the magic predicates in the compiled grammar
magic makes filtering explicit through characterizing it as definite clauses
there is however no reason to keep this rule in the magic compiled grammar
note that an abstract seed can be derived from the user specified abstract query
figure NUM magic compiled version NUM of the grammar in figure NUM
NUM create a seed fact magic q NUM from the query
ill the initial stage n a is set to the frequency of a appearing on its own and t a and c a are set let us calculate the c value for the string wall street
null b is every word sequence that contains a n b is the number of occurrencies of b as a result they do not extract the sub strings of longer collocations unless they appear a significant amount of times by themselves in the corpus
both of them l eporter of the wall street journal and staff reporter of the wall street get c value o since they q pear with the same l requeney as the NUM gram that contains the re
l his paper NUM rovidcs an at l roa h to tim semi aul onmtic exl i action of olloca ijons flom eorl ora using sl atisti s
NUM f r the NUM grams there is one appearing with a l requency rcb igger than that of the NUM gram it is nta incd in of the wall street jourlml
collocates strongly sltggesl s tie rest of the cellocat ion ulfited could ilnply states or kingdom null the classiiics collocations into i redicative relations rigid noun phrases and phrasal telnplatcs
NUM wall street analysts appears for the first time so it gets its c value from equation NUM wall street journal mnl the wall street appearing in longer extracted n grams get their values from equation NUM
lb this we add that the sut string ai NUM ears in more than one an li lat eollo ations evell if it h es not appear by itself
the following example illustrates the problem and their n proach consider the strings a in spite and b in spite of with n a and n b their numbers of oceurrencies in the corpus respectively
these models can be further classified into three types of probability models according to the type of values each random variable
our experimental results indicate that for seine class of verbs the accuracy achiew d ill a disa nlbiguni ion
crucial issue in addition to the methodology ff learning case frame patterns as probabilistic dependency graphs
we found ijmt no two case slots are determined as dependent in any of the case frame patterns
this would require even more data and thus the i roblenl of how to collect sufficient data would be a
it is possible that more complicated probabilistic dependency graphs like bayesian networks would be more appropriate for representing case frame patterns
assuming that random variables case slots are mutually independent would drastically reduce tile number of parameters
the independence assumption can also be made in the case of a class based model or a slot based model
for each input symbol and corresponding state set an earley parser performs all three operations exhaustively i.e. until no new states are generated
the probabilistic reflexive transitive left corner relation rl rl g is a matrix of probability sums r x gl y
forward and inner probabilities not only subsume the prefix and string probabilities they are also straightforward to compute during a run of earley s algorithm
computational linguistics volume NUM number NUM the approach taken here to compute exact probabilities in cyclic completions is mostly analogous to that for left recursive predictions
convergence is guaranteed since the ex values are monotonically increasing and bounded above by the true values p x e NUM
note that this is another recursive problem since x itself may not have a null production but expand to some nonterminal y that does
we suspect but have not proved that the earley computation of forward probabilities when applied to a cnf grammar performs a computation that is isomorphic to that of the lri algorithm
an important detail here is to ensure that all contributions to a state s c and are summed before proceeding with using that state as input to further completion steps
in particular under the reasonable hypothesis that humans are not in general reversewired a it is easier to process serial orders thml their reverse
frequency in languages that lk ense cross serial lependencies than in those tha t t i ii ot
the two languages induce different dependency relationships which is best described as nesting in the context free case and cross serial in the indexed case
tit inetagralnmatical approach to dealing with cross serial dependencies involves the ussulnpl ion of an operation for testing string duplication
our approach to accounting for the processing complexity that the string duplication languages should take does make empmcal predictions and these can lie teste d
that the next inaetive edge of the same category if one is expected will have to span an dent el1 string
parsing ww languages requires at worst lie worst ease complexity of parsing w in whichever language class w is restricted to
table NUM gives an informal ranking of the language classes with their corresponding worst case recognition complexity on the standard model of computation
also in most cases the structural descriptions that underlie strings of a language are of more interest than the string sets themselves
the complex notion of transitivity is defined and the relationship between transitivity and information foregrounding is explained
the present study focuses on the semantic features of transitivity rather than associated syntax
i washed the dishes i washed some of the dishes
the study is currently at the end of the coding stage for the information retrieval experiment
j individuation refers to the distinctiveness of the object from the agent and of the object from its own background
the relationship between transitivity and foregrounding has potential for text processing in particular information retrieval and automatic summarising
on their patients e.g. he kicked the door he opened the door
for the automatic summarising experiment ten articles are taken from the corpus at random
the feasibility of using transitivity as a tool in text processing will be assessed by two experiments using the same corpus
e an action is more effective if it is volitional e.g. he bought the present he forgot the present
the preferences that grammatical relations assign to conceptual relations drive path selection taking into account the specific syntactic context in which a semantic composition is to occur
however with the join method the algorithm identifies NUM possible chains that satisfy the preferences attached to preposition des fig NUM
to reduce search tile link resolntion strategy does not consider all possible chains and implements the first two criteria directly in the chain production step
the basic idea is to project the two head concepts onto the domain knowledge and find a plausible concept level relation between the two
the distinction between local information aim information inherited through the hierarchy in filrthermore exploited when ranking different chains between two concept types
this representation connects words or predicates with grammatical relations such as subject object oblique object modifier etc
2notice though that these types are strongly linked by relations other than is a through the knowledge base models
wc can use three methods of increasing complexity to find chains to link c1 and c2 NUM concept fllsion the two concepts may be redundant
type coercion assumes that the t redi ate drives semantic eompositioll and that the semantic representation of the argument inllst adapt to it
fhe transducer maps the string xaxayby to xaxbyby following the path NUM NUM NUM NUM NUM NUM NUM NUM and the string xbybyxa to xcybyxa following the path NUM NUM NUM NUM NUM NUM NUM NUM
thus we can construct transducers directly from replacement expressions as part of the general calcnlus without invoking any special rule compiler
consequently regular expresmons can be conveniently combined with other kinds of coperations such as composition and union to form complex expressions
conditional parallel replacement corresponds to what kaplan and kay NUM call batch rules where a set of rules replacements
however some of them can be expressed more conveniently in the above way espe cimly when tile replace operator is used
NUM and therefore replaced by li or it is mapped by and not replaced
the middle x would be mapped either by an ri or by an li but not by both at the same time
this choice of words is motivated by the linguistic tradition of writing the result of a rule application underneath the original form
NUM since we have to use different types of brackets for the replacement of empty and non empty upper el
the index that the subject bore in the input is assigned to an optional prepositional complement in the output
when applied incrementally this procedure yields all solutions fulfilling some of the criteria first
tgl defines a general format for expressing production rules as precondition action pairs cf figure NUM
in french it is however difficult to assign absolute priorities in the same way since we can find both types of constructions in similar contexts
if stylistic preferences observed in the corpus have to be reflected in the automatically generated texts a reasonable solution would be to select indifferently one of these rules
following meteer s analysis the lexeme dry in 7e denotes a property which can not be realized if an event perspective is taken on the predicate
the authors provide in particular some examples where a manner attribute is realized as an adverb in english while incorporated in the verb in german and french tu
the choice of a more general verb relies on the same rules but the generation process will proceed from a transformed input representation built on a superordinate predicate
according to our corpus this kind of rephrasing operations will normally concerns only the english versions since in the french procedures specific verbs are systematically prefered
5for sake of clarity we consider that the illocutioncry value is always imperative since we strictly focus in this paper on the instructional parts of the procedures
for example it can be the instrument as in the verbs lockwire energise and pressurize or a locative argument as in the verb jack up
the predicate will be directly linked to tile verb remove as specified in the lexicon and the three arguments will be realized at the deep syntactic level
knowledge and lexical semantic inferences are involved in these evaluations and they require a deeper model of domain knowledge and precise semantic definitions of lexical items
written as fully specified relations between words rather only what is supposed to be changed is specified
ulti odal drawhtg tools should support level d and NUM the most dill cult levels to meet ordinary users rcquirenm l s
ilfformat ion of inputs such as input a rriva NUM time a nd the interva NUM between two inputs is importa nt in interpretitlg multi rood a NUM iuputs
inellhodo ogy i ha i a llows nol olliy ol je ts but also event l ased progra nuning
writing after tlu a nalysis about forty expressions were selected a ud va ria tions of ea ch selected expression were a lso genera ted
the computatiollal met aphor we prefer is nol chat of objects but rather that of l rocessing the stream of events in a gra nuna tical
tlire oul o sillil le l uihihig NUM locks or a li a rchilecl t i l lcb i ligjes
the representations of a natural language processing system have to account for this fact
w hamo cxperinl nled with NUM synthcticly goner a tod sots of trees with the propej tios given in l i lc NUM lit this l mqe lcb tie third cohllilll label all gives the tvera gc
twelve fifteen could be the time a quarter after twelve the price one thousand two hundred and fifteen the room number one two one five and so on
do n t we can not possibly find tile referent for twelve fifteen unless we know we are in a subdialogue discussing flight times and arrival times have been previously mentioned
earley chart as constructed during the parse of aaa with the grammar in a
may imply an infinite summation and could lead to an infinite loop if computed naively
for grammars without cyclic dependencies among e producing nonterminals this procedure degenerates to simple backward substitution
there the extended left corner relation is used for top down filtering the bottom up application of grammar rules
the top down prediction alone generated NUM NUM states and parsed at a rate of NUM milliseconds per sentence
this fact can be used to substantially reduce the cost of the matrix inversion needed to compute r
andreas stolcke efficient probabilistic context free parsing matrix with rows indexed by nonterminals and columns indexed by terminals
in semnet this ambiguity is resolved by attaching the following quantification a labels to arcs universal u refers to the instances of the concept and says that all the instances of the concept are involved in relationship specified by the event
however this solution can not be applied generally because all coordinations have not such natural intersection see NUM
i know who i ask for a bike and for a fishing rod 4b 3e sais qui les demander
in the case of gapping structures the subject la and or an extracted element lb is present in the two sides
this verb must subcategorize for each constituent of the coordination and this is not the case in example 2c 2e
underspecification of the different categories would lead to accept the six examples or wrongly reject 2d according to the descriptions used NUM
it might appear suhicient to check all the events attached to a node to determine whether a default al l lies but it shouhl be remembered that events can also be inherited from far ul the inheritance hierarehy
the re implementation made NUM errors under the above conditions
the impacl of different alignment strategies should repay study
we refer to this test set as sul1136
a possible pronunciation for the input corresponds to a complete path through its lattice from start to end with the output string assembled by concatenating in order the phoneme labels on the nodes arcs
also the pseudoword shead receives the bizarre vowelless pronunciation f d where NUM denotes the null phoneme when using the d n model and the twb database
this problem is entirely avoided with the sullivan and damper style of lattice because the shortest length arc corresponds to a single symbol mapping rather than to a bigram which may be unique
for the d n and prod models paths were pruned when their length exceeded the shortest i und so far for that input leading to a uselul reduction in run times
the problem here is not that there is no aa bigram in the dictionary which is found in words such as bazaar but that it only appears towards the end of other words
in this translation the deference politeness strategy is transferred to the hedge words well and sort of the solidarity politeness strategies are transferred to the tag question is n t it and the subject predicate inversion is transferred into the extraposition construction
pba has obvious application to text to speech conversion by machine
we refer to this test set as d n NUM
unlike a slip of the tongue repair where the speaker would have deleted the original phrase had there be time and means in an elaborating repair deleting the phrase on monday in the example above would result in a different effect on the dialogue
purported obj involved obj pat motivated by before state
one possible solution is of course to dispense with the idea of using a general grammar and simply code a new grammar for each domain
mccord interleaved parsing with pruning in the same way as us but only compared constituents over the same span and with the same major category
furthermore it applies equally well to lattices rather than strings of words and can take account of acoustic plausibility as well as syntactic considerations
using only non phrasal rules compilation of the tables for a NUM NUM example train null ing set required less than two cpu hours on the same machine
potentially as with any generate and test algorithm this can mean efficiency is reduced some paths will be explored that could in principle be pruned earlier
it has a to auxiliary as does the vpe
we show the result of this combined cow rage of disainbiguation
one major problem we identified was the frequent use of passive constructions which the shallow parser was not able to process
we would like to thank the following people for fruitful discussions hans uszkoreit gregor erbach and luca dini
NUM to aug NUM each consisting of about NUM messages from which NUM to NUM messages are about fighting actions
disambiguation of the morphological output is performed by a set of word case sensitive rules and a brill based unsupervised tagger
NUM is the familiar as in which the vp antecedent xp contains a sloppy pronoun yp
thus tim above examt le will be notated as follows NUM l omj loves his at
NUM when harry drinks i always conceal np my belief that he should n t vp
this allows an extremely simple account of the recovery mechanism involved in sloppy identity the elided vp is exactly identical to its antecedent
while anaphoric expressions are normally thought to be identical in meaning to dmir antecedents they receive a different interpretation than their antecedents in these cases
in this section we show thai the linear precedence rules in gpsg can be derived fl om the assumption that the linear order among different types of modifying phrases such as np pp and cp should minimize the structural complexity so that the sentence is as easy to process as possible
following the standard formulation of lcg we regard the standard lcg connectives and v as directed implications so we construct our system so that a fl can combine to form a if fl is logically stronger than
NUM as we have already said the use of denominal verbs often causes differences between the french and english versions of instructions since they are usually not available in french
2f is the closest translation of the english version 2e this remark holds for all the examples given above
vu rthermore ge eral domain thesauruses do not over lommn specifie terms
in this paper words that are not contained in the thesa urus
positioning unknown words in a thesaurus by using information extracted from a corpus
thesauruses are among the most useful knowledge resources for natural language processing
tile wor t occurs NUM NUM iines in our corpus
figure NUM NUM shows exa mples of the rela tlonshil s
r nd l rotect hulua u fighter
mat have t o be integra ted
ca re which finds a u a ppropria te
fits multi lnodm systems whose multi moda l
this is a serious shortcoming a pba system should always produce a bestattempt pronunciation even if it can not produce the correct one
while this may have led to the pruning of a path contributing to the best pronunciation its contribution would be very small
this continues until the two are right aligned i.e. the number of right shifts is equal to the difference in length between the two strings
glushko for instance showed that exception pseudowords like tave take longer to read than regular pseudowords such as taze
information extraction systems are domain specific because they extract facts about a specific domain and typically ignore information that is not relevant to the domain
for example in figure NUM two noun phrases are identified the world trade center and terrorists
both text classification systems were trained on the same set of NUM texts and were identical except that they used different concept node dictionaries
it is interesting to note that approximately half of the concept nodes in the autoslog dictionary were proposed fewer than NUM times by autoslog ts
however this pattern is also likely to appear in texts that describe other types of incidents such as accidents and military actions
in previous experiments we used autoslog to construct a dictionary for the muc NUM terrorism domain using NUM relevant texts from the muc NUM corpus
next autoslog uses a small set of heuristics to infer which other words in the sentence identify the role of the noun phrase
in contrast a dictionary for information extraction requires patterns that will extract relevant information but they may also extract irrelevant information
to achieve high recall the threshold values must be low which allows some irrelevant patterns to pass threshold and cause erroneous classifications
where empathy is a grammatical property that indicates the speaker s position in describing a situation
this association gives us the expeeled result the discourse referent e or s is in the scope in the sense of drs construction of the possible negation
our answers are first some french data suggest that durative complements with negation should not always be seen as demonstrating the din alive ity of the predicate second kamp and reyl
tile tagging was done manually and the error rate measured by the authors is around NUM for polysemous words
this paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the brown corpus
2the senses of a word are represented by synonym sets or synscts one for each word sense
thc system needs to know how words are clustered in semantic classes and how semantic classes are hierarchically organised
there are some factors that could raise the performmace of our algorithm work on coherent chunks of text
nominal relations include also three kinds of meronymic relations which can be paraphrased as member of madeof and component part of
these other relations could be extracted from other knowledge sources both corpus based or mrd based
the results for a window size of NUM nouns are those shown in table NUM
it does not need any training and uses word sense tags from wordnet an extensively used icxieal data base
two types el results can be obtaincd the specific scnse or a coarser file level tag
NUM for the word based or class based models case slots are judged independent with the data size cm renl iy available in the penn tree bank
given a corpus of preclassified texts a sentence analyzer circus is applied to each sentence to identify all of the noun phrases in the sentence
it is known that using log bits to describe each of the parameters will approximately minimize the description length 1rissanen NUM NUM
base line refers to tile method of always ttaching prep noun to noun1
NUM if not in levin find synonym set from wordnet
tony broke the window with a hanuner
see table NUM for the full results
NUM for each targeted noun phrase autoslog finds the sentence in which it was tagged NUM and passes the sentence to circus for syntactic analysis
in the subject case consider the sentence john smith killed two people and the targeted noun phrase john smith tagged as a perpetrator
finally these theories provide no plausible explanation of how irony is discriminated flom non ironic echoic utterances
under the process a study should be carried out on building a stochastic language model using both syntactic and semantic information for speech understanding
includes many previous theories claiming that irony ommunicates an ironist s emotional attitude
on one hand we have extended the feature structure unification to disjunctive and set values in order to check the compatibility and the satisfiability of subcategorization requirements by structured complements
an interesting book and which i will enjoy to read NUM je demande pierre son v61o et marie sa canne p che
i ask peter for his bike and marie for her fishing rod 4f a pierre je demande son v lo et ps marie sa canne p che
the classical typology of coordination i.e. coordination of constituents NUM and of non constituents hides some regularity of the phenomenon as it focuses on concepts of constituent and syntactic category
one structure corresponds to a coordination of sentences with a gap of the verb after el the other one consists in taking the coordinate parallel sequence of constituents as only one structure
i ask peter for his bike and mary for her fishing rod NUM pierre vend un v61o et donne une canne k p che g marie
two structures are available in case of conjunction reductions
our proposal is then focused on this extension
the priming words which are consistent with the current semantic context are therefore favored
experimental results which suggest the suitability of this model are finally provided
in a first part we describe the semantic knowledge our parser relies on
contextual adaptation each cell of the second layer represents a peculiar semantic field
the parser aims only at extracting an unfinished microsemantic structure pragmatics should then complete
as illustrated by the previous example the microsemantic parser masters rather complex sentences
section NUM makes some closing observations
null we call a basic focusing cycle the cycle that includes the focusing algorithm followed by the interpretation of anaphors then by the evaluation of the proposed antecedents
a minimal cs is a template comprising a predicate that identifies the basic type of the represented event and a set of roles or predicate cases
mow mor past tense sing one mow ed
second hypothesis the initial ee of a well formed first sentence does not contain non prr pronouns just as an initial simple sentence can not
types node path and node path pair
triggering rules are evoked by words of the sentence and allow the activation of well formed cs templates when the syntactico semantic filter is unified with the syntactic tree
the complete steps are given below see also figure NUM step NUM split the sentence i.e. its semantic representation into ees
step NUM process the next sentence until all the sentences are processed split the sentence into ees apply step NUM then step NUM
using pronouns reflects what the speaker has focused on in the previous sentence so that the focus is that phrase which the pronouns refer to
they concern mainly the way of splitting embedded sentences and the problems of determining the theme and of managing the other ambiguities and the several readings
for example to process pronouns of the sentence NUM split into two ees see below the algorithm must consider ee2 before eel
structure without regard to the implications for the hearer
second a word is choseu conditional on each tag
he probability space underlying sehl ence structure
this prcvenls us from assend ling c in umltiple ways
in the version of 0t one level ot ellison incorporated into his system outputs of gen are constrained 4ellison uses only one type of mark and determines rank ordering from the relative positions of marks for each output segment
search complexity will however be an issue in the implementation of the system after an initial brute force implementation work must be focused on determining how the harmony marks can be used to heuristically guide the parser search
for the tagalog example gen will output the regular language shown in figure NUM for the first three candidates umgradwet gumradwet and grumadwet NUM each candidate consists of segments associated with a syllable structure position and a morpheme structure marker
the additional computational complexity for implementing this type of system may be quite large the search space for determining unknown strings at parse time will make for a slow implementation unless suitable heuristics are found for searching over each type of string
it allows bottom up filtering that achieves a goai directedness which corresponds to dynamic top down evaluation with abstraction and subsumption checking
NUM magic sentence decl buys j a b m
the variables are bound to instances or atomic values of the sitspec when the two are matched against each other
the generator assumes a language neutral level of event representation the situation specification or sitspec
to encode the valency information we use the partial semspec of a lexicon entry
the inner probability NUM is not used
thus within the context of our system we define aktionsart features in terms of patterns of verb denotations
from which the closed form solution is derived
the criterion of optionality as indicated above singles out the obligatory complements
the sitspec denoted by the first configuration is the water drained from the tank
null covering the subset of the denotation nodes that are actually expressed by the lexeme
the proportion of cases in which the procedure was forced to guess either because no data supported either analysis or because both were equally supported is quite low
results obtained with this specific sut t ort fun tion NUM NUM are sumntarize d in table NUM
and NUM performing too much iterations can produce a more probable solution which will not necessarily be the correct one
vi determiner v i adjective vi NUM nou r0
we can state that in all experiments the refinement of the model with hand written constraints led to an improvement in performance
second reason is that relaxation is not an algorithm that finds global optima an NUM can be trapl ed in local maxima
relaxation labeling is a generic name for a family of iterative algorittuns which perform function optimization based m local infi rmation
in ge ne ral constraints must be written mannally since they at the modelling of the problem
departamenl de llenguatges i sistemes informgtics universitat polit6cnica de catahmya pan gargallo NUM NUM barcelona spain padro lsi upc
maxinfizing global consistency is defined as maxi i i is the weight mizing j t j x sij vvi
this kind of interference is hereafter called effect precondition e p conflict
as a special case time point t is used to represent unbounded persistence
the effects and preconditions values respectively are sets of triples representing the action s effects and preconditions
thus equation NUM is transfornted to
examples of results by the extraction method
thus equation NUM is reduced to
q2 what speech act pattems have local cohesion
figure NUM shows a dialogue with our utterance model
in section NUM our statistical method is presented
table the end of expressions speech act type
it has been implemented in prolog and is used for plan recognition in dialogues
part d without local cohesion no turn taking
part b with local cohesion no turn taking
this method in particular makes a c possible
an edge is called active if its rsubaetions vahm is a non empty sequence and is inactive otherwise
thus we need to resolve problems in each sentence in the context model ill lividumly
thus our system analyzes them as verb phrases and nominalizes them in the translation
parses were joined into a single stru ture by using ilfformation in the context model
in either approach document classification using words has problems as follows NUM words in the documents must be normalized for matching those in the dictionary and the thesaurus
the style of the documents the style of tensei lingo is similar to that of an essay or a novel and it is written in colloquial japanese
in comparison we suggest to combine further the corpus and the mrd by use all the corpus examples of the mp d definition words instead of those words alone
we tested the algorithm on the treebank NUM corpus which contains NUM million words from the wall street journal NUM and is considered a small corpus for the present task
the sentence was the american people and their government also woke up too late to the menace drugs posed to the moral structure of their country
NUM to avoid poor estimation for words with a low count in the training set we multiply the log likelihood by min lcb NUM co t w
table NUM shows the most similar words found for the words with the highest weights in the drug example low similarity words have been omitted
for example trafficking was found to be similar to crime because in the drug contexts the expressions drug trafficking and crime are highly related
here we let denote the size of the noun partition and q the size of the verb partition
assumption that similar words occur in the sa me context with roughly equal likelihood as is made explicit in equation l
tlere coverage refers to the proportion in percentage of the test patterns on which the disambiguation method could make a decision
purely as a method of estimation as well the superiority of mi l over mle is supported by convincing theoretical findings c f
standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words
the simplicity of the approach means the model trains on NUM NUM sentences in under NUM minutes
question NUM are the hjth word and the jth word adjacent
the idea is to back off to estimates based on less context
in this way it is similar to an argument or adjunct
s is the sentence with words tagged for part of speech
NUM is then maximized instead of NUM
we can not in this short text review all aspects of this integration and will therefore mention only the most rele vant points
we show in this last section that both representations fit together in the framework of a bottom up construction procedure which allows a satisfactory computational treatment of negation
in the second sentence of 5a the simple past introduces a new event which is localized after tile event introduced in the previous sentence
thus one can easily imagine that 4a refers to a gesture or some kind of behavior that could be interpreted as a refusal
we are intercstc d hc rc in the coml ositiolml con sl rll tioll of s un ulti represcn 4dfions
elb sl atcs somct hing like there is no evcnl of jean s stot ping al l he time t k lb introduces a
one of the reasons wily we may prefer the representation klb to k b is that it is simpler from the technical point of view
tion of the marks assessed each of the partial descriptions for the factor categories plus any additional marks incurred as a result of the structure added by the production itself
the user can gesture and draw directly on the map with the pen and simultaneously issue spoken commands
it supports a fully implemented usable system in which hundreds of different kinds of entities can be created and manipulated
these are created by drawing the appropriate spatial feature on the map and speak null ing its name
this paper describes a multimodal language processing architecture which supports interfaces allowing simultaneous input from speech and gesture recognition
location xcoord NUM command point figure NUM point interpretation of gesture command icoor it NUM NUM NUM location NUM NUM NUM NUM inc figure NUM line interpretation of gesture
this work is supported in part by the information technology and information systems offices of darpa under contract number dabt63 NUM c NUM in part by onr grant number n00014 NUM NUM NUM and has been done in collaboration with the us navy s nccosc rdt e division nrad ascent technologies mitre corp mrj corp and sri international
as such it is ideally suited to the task at hand in which we want to determine whether a given piece of gestural input is compatible with a given piece of spoken input and if they are compatible to combine the two inputs into a single result that can be interpreted by the system
xcoord NUM NUM the task of the integrator agent is to field incoming typed feature structures representing interpretations of speech and of gesture identify the best potential interpretation multimodal or unimodal and issue a typed feature structure representing the preferred interpretation to the bridge agent which will execute the command
the ink is sizenormalized centered in a 2d image and fed into the neural network as pixels as well as being smoothed resampled converted to deltas and fed to the hmm recognizer
NUM col NUM and generated the corresponding sequence of classes fig
the two key challenges for such an approach are firstly ensuring that the additional assumptions are appropriately used otherwise invalid reasoning will follow and secondly ensuring that a proof term appropriate to the original type combination is returned
as dis usscd in the introduction tim above method is proposed as a generm method for parsing ategorial systems via a trant ormation of formulae tom the relevatfl system to linem tbrmulae
the differential status of the left and right hand side formulae in a sequent may be addressed in terms of polarity with left formulae being deemed to have positive polarity and the right formula to have negative polarity
deriving the argument yc z of the higher order fornmla involves a final introduction stei whk h semanl ically corresponds l o an abstraction step that binds the variable semantics of l he additional assuinption z
in that case we can see that all hough c olnpilation has eliminated the need tbr an explicit introduction step in the proof the sl ep still occurs imtflicitly ill the semantics
the second challenge we noted for such an approach is ensuring that a proof term loosely the snore the requirement that t is a proper subset of which will have the consequence that other assumptions must also contrihute to deriving the argunwnt b this will block a derivation of the linear logically valid xo yo y x
2al l roa hc b include sequent proof n n mtlisacion methods for i k6nig NUM lcb NUM flel ple NUM llemlriks NUM charl pro sing mc l hods for l t 6nig NUM hep
the crucial problem for developing a chart like method is the fact that in combining any two elements a b c there is an infinite number of possible results c we could derive and that what we in fact should derive depends not just on the formulae themselves but upon other formulae that might combine with thai result
an initial state which does not correspond with any pair is also created
since we do not allow the feature to stack tile string based method does not yield the full expressive power of indexed languages
more sa h imrl ial pal t erns ntay ice specified i actern unifical ion leads co all egm consticuenl ombinal ions
since most grammatical constraints in iipsg are expressed via structure sharing and fuf uses pointers to indicate coreferences most of the processing time was spent in following pointer chains through deeply nested feature structures
it otd ains l he argulnent st ru l ure of t he exemes aim links them i o l ossibly prelixed stems
before we describe the integration task we will briefly sketch the mmn characteristics of these resources emphasizing those aspects which either cause problems lbr integration or provide the means for perfi rrning the integration task
each argument must be checked during generation if it is unifiable with the slash specification and if so it has to be made coref erential with slash
ed integration tasks on two different levels integration of software sys elns by c mt itjing fuf with x2morl we have extended tin2 flmctionality of fuf
i uring the finm linearizati li the cxl ended ewe l wel rules map l he on acenated stems and affixes to the appropriate surface strings
a and a correspond to the two peaks in the frequency distribution of k u niu v in figure NUM the two parameters can also be interpreted as the percentage of true and false positives
nevertheless it appears that our word to word model with only two link classes does not perform any worse than ibm s model NUM even though the word to word model was trained on less than one fifth the amount of data that was used to train the ibm model
to retain word type pairs that are at least twice as likely to be mutual translations than not the threshold can be raised to NUM conversely the threshold can be lowered to buy more coverage at the cost of a larger model that will converge more slowly
the majority of indirect associations can be filtered out by a simple competition heuristic whenever several word tokens ui in one half of the bitext co occur with a particular word token v in the other half of the bitext the word that is most likely to be v s translation is the one for which the likelihood l u v of translational equivalence is highest
p wk s wk lt l e vwk ltk NUM ensures that the model halts with probability one
this paper proposed a method for extending an existing thesaurus by classifying new words in terms of that thesaurus
however as mentioned in comparison with uramoto s work the use of other relations should be investigated
the probability of each noun in the test data being classified into each of these NUM cluster was calculated
the errors included the following five occurrences of coke were not recognized as entities
new york times and coca cola within coca cola classic are mistakenly recognized as companies
the lexical rules are also used to recognize names of organizations persons locations time expressions and dates
most of the correct fillings can b e attributed to its successful analysis of NUM and NUM
it did not reach a relatively stable state by the time of the formal testing on october 6th
the formal testing results for ne co and te are quite close to the training test results
however when it fails to do that it retrieves parse fragments that cover the complete sentence
principar takes the lattice of lexical items and output a dependency tree between the words in a sentence
since the lexicon is derived from machine readable dictionaries it contains many obscure usages of the words
the properties associated with each instance of a domain entity are extracte d by the subtree pattern matcher
the central ol rcb ecl coordinating all these modalities iiitlst sm l ap propriate messages at approl riate times to ea cll r the drivers of the wu ious devices and theu iimsl syuthesize the answers that are received
this sys tern then synthesizes that information and produces a query such as qu l l lcb y func of object l inosaur bitmap NUM ntenu item i i v which it is progra mnmd
l hinldiig el the illotise l s it physicaj eiltity for the l rogr illl iller to lise iliitkes perfect sense btlt viewing a iiiollse lick as a li object seeliis less conipelling
if there are three triodes u m2 attd m3 a nd the mjmbers of granum u rules imperative meaning action object verb action object object
l iinkhig o the coiilpiiter screen the whidows oil l ha l soi eeli itnd eveli the bits in those whidows a s shnple objecls coiiil osed togelller int o a i owerletil editor has l eell a i x l reniely coinpeliing vision for iril er lx e designers
relative importance of words in common we also looked beyond the initial use of lexical items to determine what role was played in subsequent conversation by the words that agent and client both used
since clients were not concerned with social standing including establishing mutual linguistic conventions accommodation in the machine interpreted setting was a local phenomenon which did not extend throughout the conversation
exploring the linguistic behavior of humans interacting with computers in an unrestricted environment allows us to determine how humans are naturally inclined to accommodate to the current limitations of human machine interaction
figure i wordnet senses for the noun book
figure NUM instances for the type animalofood
in the machine interpreted setting we saw a rate of accommodation higher than that of the human human setting but lower than that of the human interpreted setting as expected
there are no compelling reasons to exclude other kinds of information but for now we base our basic design on ps which only includes these three in its definition of qualia structure
determine file extent of coincidental overlap we measured the lexical overlap in the speech of clients and agents from the first experiment who had not participated in the experiment together
these and other decisions are made on the basis of verbalization parameters by the surface generators
the rule is defined similar to the one given above so we do not show it here
but how exactly can we motivate the distinction between optional participants and circumstances in our framework
an event without such an activity is a mere state transition e.g. the room lit up
the optional elements are listed here because they can be verbalized with the extension rules that we have introduced
the syntax is of course the same as that of the denotation of a lexical entry
in the class of non durative verbs we find amongst others the opposition between iteratire and semelfactive ones
returning to the example of to drain figure NUM shows how the rules successively derive the various configurations
this is necessary since we want to break up the internal event structure in the representation of verb meaning
durative verbs characterize continuous occurfences that do not have internal structure like to sleep to sit
they reported that the word expert for throw is currently six pages long but should be ten times that size making this approach impractical for any system aiming for broad coverage
sense tagging the automatic assignment of the appropriate sense from some lexicon to each of the words in a text is a specialised instance of the general problem of semantic tagging by category or type
the transitive subset of the linking relation can be implemented as follows
of course such clauses lead to a highly inefficient search for prolog
np det n np agr n agr np agr det agr
the rules account for sentences like the child s father sleeps
automatic compilation of the linking relation employed in certain parsing algorithms for context free languages is examined
consider the lc analysis of the sentence john loves mary
moreover they discuss linking only with regard to filtering
the former uses categories which have been assigned to documents when constructing clusters while the latter does not
how was this integration accomplished so relatively easily
this specialization is there to prevent the learning task from becoming too complex
the chunk relation finder determines how a chunk relates to its parent chunk
all neural networks have one hidden layer and are conventional feed forward networks
results for an unseen independent evaluation set are shown in figure NUM
the chunk n label principle does not incorporate any mechanism for this
several methods have been suggested compensate for these speech related problems e.g.
if low hand labor is involved then it s even better
then the search finds the most probable and consistent feature structure
two no clear and quantitative statement about overall performance is made
with regard to accuracy we merely wish to demonstrate that for statistical mt accuracy is not significantly compromised by substituting our efficient optimization algorithm
the noun phrases are then filled into a template of the form 0o you mean npl np NUM or pn
when the procedure assigned to a given signature is evaluated the general manager passes the parameters on to the agent
for the purposes of this paper a new word noun not appearing in the thesaurus is treated as a new document and a word class in the thesaurus corresponds to a predefined document category
s do you mean carnegie museum of natural history andy wartiol museum or fort pitt museum
although this approach significantly decreased the need for manual intervention about a hundred examples had still to be tagged manually for each word
in iteration NUM menace was learned to be similar to other narcotic related words yielding a small advantage for the narcotic sense
in summary our algorithm achieved performance comparable to some of the best reported results using much less data for training
in that work the definition words were used as initial sense indicators tagging automatically the target word examples containing them
introduction word sense disambiguation wsd is the problem of assigning a sense to an ambiguous word using its context
on the word drug our algorithm achieved performance of NUM NUM after being trained on NUM examples contexts
for each example s of the narcotic sense of drug the value of sims s narcotic increases with n
NUM NUM millionen dollar NUM percentages NUM NUM prozent zwslf prozent NUM dates NUM
we will show how this facility can be used for all efficient and consistent treatnlent of all kinds of messy details
any non deterministic backtracking algorithm depth first is badly effected by ambiguities as it has to redo repeatedly large amounts of work
one result of the corpus investigation was that NUM of the sentences in the corpus have between NUM and NUM words
the head value is shared between head dtr and mother the base value in addition between head dtr and functor
though this is at the cost of expressiveness it is claimed that by leanness hnguistic felicity does not suffer
in the default case this is the information which is input to the th ls component text handling to linguistic structure component
f exical information is distributed over three lexicons null a tl lexicon which contains information relevant for seglnentation cxdusively
januar NUM NUM acronyms and abbreviations dasa cdu gmbh etc
this paper reports of a project ls gram which tried to make a step further in bringing ugs closer to applications
designating this category a this means all non lexical productions are of one of these two forms a aa a a aa a rcb the degree of word order flexibility is the critical point
an itg consists of context free productions where terminal symbols come in couples for example x y where z is a chinese word and y is an english translation of x NUM any parse tree thus generates two strings one on the chinese stream and one on the english stream
the scope is indicated by curly brackets
we are now constructing a larger volume of corpus to address the second problem
that is the method generates redundant substrings which are subsumed by longer strings
in this phase we developed a new technique for extracting only useful chunks
we would like to thank all people concerned for providing us with the tools
the third step iteratively evalnates the bilingual similarity of word chunk combinations by using the above trees
only word level coincidences are extracted by introducing the explicit word delimiter
to extract a reasonable number of nouns that are indicators for the senses of target adjectives one straightforward approach would be to extract a representative sample of sentences for each target adjective to disambiguate each target manually and to extract those nouns that are relatively frequent and that are modified by the target in one sense but not in the other
the number of significant indicators recovered is quite variable ranging from none for the not soft sense of hard the not dark sense of light and the not tall sense of short to NUM for the not young sense of old and NUM for the not wrong sense of right
expanding to include all nouns from the co occurrence sentences that substitute young more than new for old almost all added nouns continue to be for human beings as well as certain pronouns l we you he she and me us her him to which animals plants and body parts are added
thus the adjective old may apply either to the relationship or to the role designated by doctor friend empress and nun with old having the sense former or of long standing i was with old friends i had made new friends and that night i think that i was lonelier than ever before
computational linguistics volume NUM number NUM there is an inherent ambiguity concerning the relation of the adjective old to its noun the referent of the noun is an individual which is animate but it is an associated noun sense to which the adjective applies which may be animate or animate that strictly determines the sense of old
in these instances it is not a physical dimension of the item that is short and reference to such dimensions in the case of book relates instead to the not tall sense of short
this sense is associated with horizontally separated members of mostly inherently paired repeated entities it is appropriate to horizontally separated paired body parts eyes ears thumbs hands wrists arms legs feet etc which constitute the majority of nouns by text frequency that are modified by the not left sense of right
he was on the right not wrong side of the screen he had an excellent day s work behind him and in two minutes time he would hear bing crosby sing
semantic attributes such as carried things would characterize nouns such as load e.g. burden cargo or freight and load bearing equipment or load relevant equipment would characterize nouns such as john s justeson and slava m
this rule allows the activation of a basic template produce4 NUM
help he gave me dunng every stage of tins work and to marllyn mantel david mitchell kevm schlueter and melame baljko for their advice on experimental design and stanstlcs i am also grateful to marzena makuta for her help with the rst analyses and to my colleagues and friends who volunteered to act as judges m the experiments described here tins reasearch was supported by the natural sciences and engineering research council of canada
rice97 summarizer the discourse based summanzauon program that we propose outperforms both the basehne and the commercial summarizer see table NUM however since ts results do not match yet the recall and precision figures that pertmn to the manual discourse analyses zt zs likely that improvements of the rhetorical parser algorithm wall result m better performance of subsequent lmplemetat ons acknowledgements
in tins paper an architecture is presented for robust and portable summansatlon
both m the same sentence and later on m other nnportant sentences
els and between the mtermechary and the surface clusters of pragmatic features figure NUM the intermediary features
other less subjective features can be fully exploited later on for the automation of the encoding of the abstract pragmatic features the vahdatwn tests also mdtcated that there was NUM agreement on which of the corpus sentences were m portant and wlnch nmmportant for the corresponding texts
note that the translation results using our parser are fairly close to those starting with a correct parse
this means that the errors made by the parser have had a relatively moderate impact on translation quality
null table NUM shows the impact of reducing the feature set to a set of n core features
therefore the NUM feature test is a relatively good indicator for the contribution of the semantic knowledge base
the proper classification is the specific action or sequence of actions that the system believes should be performed next
even if a complex translation pair does not bridge a structural mismatch it can make a valuable contribution to disambiguation
NUM NUM a subcategorization table that describes the syntactic and semantic role structures for verbs with currently NUM entries
one of these sentence is canadian manufacturers new orders fell to NUM NUM billion cana tr
number of correct constituents in system parse number of constituents in system parse recall rec
the forward looking centers of utterance u 7for the sake of simplicity here we assume that us and u arc equal
the left and right contexts for the rule consist of the intersections of the partial descriptions of these contexts defined for each branch traversed between the root and leaf node
this arises because their construction is based on the dependency model which predicts that left branching analyses should occur twice as often
the most general of these is that even quite crude corpus statistics can provide information about the syntax of compound nouns
one problem with the training methods given in section NUM NUM is the restriction of training data to nouns in af
for the pattern and two word window training schemes the guess rate is less than NUM for both models
at the very least this information can be applied in broad coverage parsing to assist in the control of search
all consecutive sequences of these words were extracted and the three word sequences used to form the test set
each test compound presents a set of possible analyses and the goal is to choose which analysis is most likely
many nouns especially common ones have verbal or adiectival usages that preclude them from being in af
using each of these different training schemes to arrive at appropriate counts it is then possible to estimate the parameters
after giving an overview of our project we concentrate on how the student s generation process is represented in our system
our linguistic model of the student s generation process essentially reflects those aspects of the second language that are currently being leamed
if syntactic mal rules were used in the parse the sentence and any relevant annotations will be passed to the response generator
written english grammar components include word order morphological modulations of words and punctuation but nothing that clearly corresponds to the simultaneous manual non manual behavior found in asl
additionally we hope to collect samples of teachers corrections and compare them to the models that will have been hypothesized
however we do not claim that every instance of an error class that could be explained by language transfer must be
transfer of such a feature i.e. when to mark tense might explain omission errors in this case of tense markings in the l2
filtering tree proof tree NUM magic rip j
for expository reasons though they are presented merely on the basis of examples of generation
these optimizations are direction independent in the sense that they are useful for both generation and parsing
the filtering tree is reversed and derives magic facts starting from the seed in a bottom up fashion
figure NUM shows the results from generation of the sentence john buys mary a book
finding these types of cycles in the magic part of the compiled grammar is in general undecidable
also with respect to the dependency constraint an optimization of the rules in the grammar is important
accuracy rate number of correct analyses number of tested utterances
we compared the two parser according on their robustness and their perplexity
such wide ellipses should nevertheless be recovered at a upper pragmatic level
coherence any lexeme must fulfil at the most a unique argument
these relations issue directly from the subcategorization frames of these priming words
most speech systems exclude therefore a complete syntactic parsing of the sentence
this paper presents a detailed semantic parser which masters most spoken utterances
this is why we based the microsemantic parsing on a priming process
it aims a double goal it constrains the speech recognition
one of the above NUM verbs whose preposition slots show dependency exceeding NUM NUM
o17 this ex porin cntation in this sccl ion
we proposed to use dependency forests to represent case frame pa terns
by our method seem to agree with human intuition in most cases
since the illllllber of dependencies that exist in a nmlti dimensiona l
to solve these problems we have developed a plan parsing method that can handle the effects and preconditions of actions and that parses plans in a manner dependent on dialogue state changes especially on the mental state changes of dialogue participants caused by dialogue utterances
a typical plan recipe for an action includes a sequence of subactions as its decomposition so interpreting an action sequence in terms of plans can be seen as parsing in which observed actions correspond to lexieal tokens and plan recipes correspond to grammatical rules
this paper describes a plan parsing method that can handle the effects and preconditions of actions and that parses plans in a manner dependent on dialogue state changes especially on the mental state changes of dialogue participants caused by utterances
whereas an imperative sentence with surface speech act type surface request is generally interpreted as a request the second utterance actually describes a step in tile plan to go to the laboratories because the first utterance convinces b that a wants to have that plan
if the procedure identifies such an action with preconditions it calls itself recursively to search for actions that satisfy them enabling actions of tile main action and then provides the action sequence consisting of the main action preceded by its enabling actions
there is another kind of interference called precondition precondition p p conflict if a precondition specified by s recipe or a precondition c i of any other component action 7i contradicts ck they can not hold simultaneously at c s starting time case d
for each recipe with its a ction a decomposition NUM effects er and preconditions p and for each most general unifier NUM satisfying constraints ei and recipe s constrains c of action i and NUM such that
a time map consists of a set of potential fact tokens a fact token is a triple tl t2 o where tl and t2 are time points and is a timeless fact description a term that represents the proposition that holds at tl and continues to persist through t2 or until a contradictory fact holds
more concretely without treating them it is impossible a to describe actions in terms of their effects b to capture the relationship between an action and another action that satisfies the former s preconditions to enable it and c to interpret actions in a manner dependent on the dialogue state
another interesting possibility is to use the lm itself as the similarity metric
we believe that it is much easier for a person to separate a set of texts into two piles the relevant texts and the irrelevant texts than to generate detailed text annotations for a domain
the original version of autoslog could have applied multiple heuristics as well but its dictionary had to be manually filtered so it was preferable to keep the dictionary small
it is possible for multiple heuristics to fire for example patterns NUM and NUM may both fire if the targeted noun phrase is the subject of an active verb and takes a direct object
figure NUM shows the intersections between the autoslog ts dictionary and the autoslog dictionary after frequency 12the dictionary actually contains NUM concept nodes but some concept nodes represent the same pattern to extract different types of objects
we ran each system NUM times using a variety of threshold settings r was varied from NUM to NUM in increments of five and m was varied from NUM to NUM in increments of one
for example in figure NUM the world trade center was identified as the subject of the sentence so all of the subject patterns are fired patterns NUM NUM in figure NUM
the primary data structures handled by bq3m are the search patterns that represent the general properties of an information to be searched for by filtering or unification within a knowledge base of occun ences
note that the determination of the sentence topic is distinct from the question of how to realize the salient cb topic e.g. as a dropped or overt pronoun or full np
bnc i o h h5 h58 bnc NUM NUM f f7 f78
some examples of the output are shown in figure NUM
clear water bay hong kong dekai c cs ust
assume the number of translations per word is bounded by some constant
in this section we summarize the main properties of btgs and itgs
this paper finds they are also useful for the translation system itself
null the algorithm is given below
a polynomial time algorithm for statistical machine translation
the translation expressiveness of btgs is by no means perfect
the algorithm above was tested in the silc translation system
lcb ulu NUM r rcb lcb ll l rcb
the results indicate that the former outperforms the latter
we then trained the parameters of probability models
both represent a coordinate structure consisting of vps
figure NUM length probability versus length
figure NUM examples of syntactic parsing
let us now define the length probability more formally
figure NUM the top NUM accuracies
iml by utilizing tim informa tiotl of global struc l tr lcb
the denser distribution of output symbols resulting from the alignment constrains the merging of states early in the merging loop of the algorithm
perhaps worse it will fail completely upon seeing any symbol other than er or end of string after a t
this transducer will flap a t after any odd number of stressed vowels rather than simply after any stressed vowel
thanks to jerry feldman eric fosler isabel galiano ronda lauri karttunen jose oncina andreas stolcke and gary tajchman
by an arc s behavior we mean its output string considered as a function of its input phoneme and its destination state
the output symbols are placed as near the root of the tree as possible while avoiding conflicts in the output of a given arc
with rea NUM lis om s s
null in our first experiment we applied the flapping rule in NUM to training corpora of between NUM and NUM NUM words
the algorithm also successfully induced transducers with the minimum number of states for the t insertion and t deletion rules below given only NUM samples
as is seen in figure NUM a transducer of minimum size five states was obtained with NUM or more sample transductions
while some modules such as morphological analysis and generation are language specific the transfer module is a common part of every language pair
this is done by clicking on the word and selecting the appropriate pattern from a list of possible expansions
this interface allows the author to work in terms of sentences rather than in terms of interconnected graphs
specifying tie NUM rcb l s at l eal illg
automated text generation requires a underlying knowledge base fl om which to generate which is often difficult to produce
these tools tend NUM o be based on a cei1tral inodel of the interface under developme nt
such a representation wouhl supt ort iterative construction of the doe lmetltation and intbrmat ion reuse
the owl NUM boxes in the figure ret resent actions anti the rectangh s represent plans
the links actually shown in tilt figure are based on the procedural relations in the domain model
tile figure for example shows the author in the process of specifying tile node save a document
for example the system automatically defines a clicking action instance for any button on the interface
actually to run and to move fast on the ground do not refer exactly to the same kind of locomotion
while global planning is largely language independent local planning can be language dependent the dictionary acts a mediator interfacing language and thought
according to the input specifications we could consider either to move to swim to walk or to run
feature structures substituting variables have to be stored in the discourse blackboard
the fact is thus captured in these procedures by checking that ei contains p instead of unifying NUM with action ei if NUM is of that form
procedure NUM let c and ei be adjacent active and inactive edges such that rsubactions e is NUM NUM
effect based action descriptions the fact that the description of the form achieve p can specify an action with p as its effect is captured by augmenting procedures NUM and NUM
given an action sequence of this form a plan recognition procedure must thus regard it as i erforming a main action to achieve its ettb ct s
fl om tile action s recipe and component actions if any the effects are essentially those specified by the action s recipe plus those of component actions
this interpretation of the decomposition relationships specified by recipes in a plan library gives us a decomt osition grammar and allows us to apply syntactic parsing techniques to plan recognition
the ending time of the action represented by an active edge is not determined yet and neither is the starting point of the effects specified by the action s recipe
now that we have the basic means to eah ulate the effects and preconditions of the action represented by an edge we can augment plan parsing to handle the situations described in section NUM
consider action a with its component actions as shown in fignre NUM let us focus on precondition b of action
constitutes an important subproblem froln a cognitive point of view which is solvable in eonl rast to the general problem
the restriction introduced above is by no means sufficient since the proof procedure is not yet sensitive to the predicates representing the readings of an ambiguous lexical item
NUM a es gibt keine sehwestern aber einige arzte haben eine mit der sic nieht verheiratet sind
let us consider the two readings of NUM which have to be expressed in english by 2a b NUM
according to the subject of the restriction used to ensure the traetabilty of the problem we have to distinguish three main approaches
the disambiguation of the ambiguous item star should make no problems given we had the right meaning postulates
in order to test this kind of incompatibility we have to demand that each resolution deduction starts with a clause from mp
on the basis of this set we can then test whether there is a consistent subset of these pieces which contradicts mp
d there are no sisters at all but some physi null cians have one to whom they are married
according to the discussion in section NUM we have to assume a fully exl ressive language for the representation of discourse
lilt many n tural languages lemenls that a n be easily deduc d by tim reader r frequently omit ed front the
of the flapping transducer some induced transducers may need to be generalized even further since the input transducer to the decision tree learning may have arcs which are incorrect merely because of accidental prior structure
the suffix of the output string beginning at position i NUM the destination state after the process of merging states terminates a decision tree is induced at each state to classify the outgoing arcs
this approach however has two problems
if the length l of the arc s output string is greater than n it is necessary to push back the last l n symbols onto arcs further down the tree
as long as the disambiguator can reducc the number of readings and the agenda is not empty a new pass is performed
we performed a test on NUM sentences from the corpus which were not used for training
also reproducing a particular bracket pair from the treebank increases the chances on reproducing its parent t inheritance
significance for recall hard first we will check whether the distribution can be modeled properly with a binomial experiment
because of space limitations we only discuss a strategy for estimating the significance level of the measure recall hard
we claim that the items listed in the previous paragraph allows a nmre flexible framework for ewduatim
in english there is a simple strategy for this remove all brackets that enclose only one word
for example the l old man is turned into the old magi
as for our test m1 and m2 can be for example NUM and NUM respectively
note that the first two types do not tell us anything about the difference between the parsing systems
this achieved NUM accuracy for four verbs out of the NUM verbs and NUM NUM in average
its a verag median and standard deviation are NUM NUM NUM NUM and NUM NUM respectively
li or NUM verl s we made an ext eriment on sense classification of verbal polysemy
a learning set of NUM tokens NUM reports from the cardiology department cardio should eliminate as much as possible errors due to unknown vocabulary
the support computing and label weight changing must be perfornmd in parallel to avoid that changing the a variable weights would affect t he support colnputation of the others
the biggest advantage of this approach is obvious only those constraints must be touched which are involved in restricting the set of possible solutions
this means that one parser is in control over the other whereas the latter one is not directly exposed to the input
i or e xample the lengths of tile links in figure NUM are shown by the numbers attached to the dependency links
our riga re work inchldc backing up th hypothesis with mph ical cvidc n e
these rules give negative votes to the parses which are not preferred or high votes to certain parses which are always preferred
most of the rule crafting was done using the general linguistic constraints and constraints that we derived from the first text ark
we have applied our approach to four texts labeled ark hist man emb with statistics given in table NUM
we consider a token as correctly disambiguated if one of the parses remaining for that token is the correct intended parse
however the agglutinative nature of the language usually helps resolution of such ambiguities due to the restrictions on morphotactics of subsequent morphemes
if a given constraint matches more than one parse of a token then the votes of all such matching parses are incremented
given a sequence of two derivation steps which can be verbalized as NUM c o by the definition of transitive closure
in this form it contains three types of linguistic information constituency structural relations among constituents and in particular the semantic categories the constituents express
for example the application program object para that stands for the logical predicate denoting the parallelism relation between lines may map in five different upper model concepts
for instance the choice of the category sentence for a constituent may lead to the insertion of the cue word furthermore in the next sentence
this not only guarantees the expressibility of the new apo but also restricts the choice of linguistic resources for set now restricted to object
the syntax of our rules means that a text structure of the form above the bar will be transformed into one of the form below the bar
many of the first nlg systems link their information structure to the corresponding linguistic resources either through predefined templates or via careful engineering for a specific application
third while most investigations have concentrated on general purpose microplanning operations we came to the conclusion that microplanning needs domain specific rules and patterns as well
while meteer associates the application program objects apos directly with so called resources trees we map apos into upper model objects which in turn are expanded to the text structures
t l where wk ltk NUM is the word parse k NUM prefix wk is the word predicted by pp edictor nk NUM is the number of adjoin operations the parser executes before passing control to the predictor the n th operation at position k is the null transition n is a function of t
in addition the derivational implementation of horizontal relations fails to produce lexical entries a s needed instead it produces lexica l
an appropriate generation of multilingual in null structions in accordance with these lexical differences can be achieved by assigning priorities to these rules
in particular the predicate lock is directly mapped to the verb freiner and the argument instrument to the phraseme ill frein
one of them in general the english one incorporates an argument which is expresse t at surface level in the french version
a verb may have a precise meaning but a restricted argument structure which may force to leave implicit some part of the initial content
besides it brings another information the nature of the locking device which can not be expressed in lie
in the corpus bilingual sentences expressing the same content may differ significantly even though closely related and acceptable versions can be obtained
the integration of such semantic representations in a multilingual environment raises several theoretical and practical problems which will be the object of future investigations
it is very di ictdt NUM o position a word with pin point accuracy
this section describes some xl eriments for l si ioning woms in isamap
ol the ca ldldates and selection f the room
are hand crafted by ol serving huge amounts of data on the usage of words
se ond the human intuition used in constrllcting thesauruses is not explicit
there are two common problems noisy co occurrence of words a nd data sparseness
in j pa n there are no free thesauruses that can i e shared by researchers
instead in this paper we give a method for determining the area in which the unknown words belongs
i would also like to thank taijiro tsutsumi masayuki morohashi hiroshi nomiyama tetsuya nasukawa and naohiko uramoto for their valuable comments
the computational complexity of parsing for tags however is o igin6 which is far greater than that of cfg parsing
we can also extend translation patterns as follows each nonterminal node in a pattern can be associated with a fixed length vector of binary features
tdmt however is based on a combination of declarative procedural knowledge sources for mt and no clear computational properties have been investigated
thirdly a translation pattern can omit the tree structure of a collocation and leave it as just a sequence of terminal symbols
repeat the addition of such patterns and assign low weights to them until the refined sequence q becomes the most likely translation of s
considerably advp NUM vp i vp i laisser vp l obj con sid rablement advp NUM
this checking can result in some subtree production deletions namely the ones for which there is no valid symbol stack evaluation
this once again shows that the recognition and parsing problem for a lig can be solved in NUM n NUM time
figure NUM algoridmi for error tolerant recognition o vertex lis sequences ii i rcb i i
we deline the distan e NUM etween two trees aeeor 1ing to the struchrral dijl cnces or differences in leaf labels
this means that paths in the trie that can lead to no solutions have to be pruned so that the search can be limited to a very small percentage of the search space
if however both trees itave a leaw s whose vertex lists match in all but the last leaf vertex lat e we
the conaputation of c uldisl x t y n invo vcs loop in whioh the minhntun is colul uted
of course many other derivation strategies may be thought of
as in the previous example we can see that the
if the resulting grammar is not empty then x is a sentence
we will show in section NUM that a similar result holds for ligs
the higher pp value for email would tend to indicate that this is the poorer lm
the results for correct and accuracy show the combined effect of the recogniser and lm
one such test is the rank correlation using spearman s s
at first glance the results appear to be intuitively satisfying
however a suitable tide is no guarantee of suitable contents
genuine texts are eliminated along with the noise
the perplexity score can therefore be used to measure textual similarity
since the email corpus is almost NUM million words it clearly meets this criterion
this effect can be mainly attributed to the source of the n grams and the extent to
this implies that quality not quantity is a major factor in training effective lms
for example sentence 2f can be paraphrased by the sentence based on the verb purger directly related to the predicative noun used in 2f 2f purger le circuit d aspiration
as mentioned before this yields a focus semantic value which is in essence i lcb ooth s alternative set NUM
we will see in section NUM NUM that interestingly instructions can be made more precise with general verbs because of differences in argument structures a general verb may have a more extended argument structure than a specific one
the generation of such sentences relies on rules rl and r NUM however in the english version it is the combination of the predicate lock and the argument instrument which is mapped to the main verb lockwire
for instance to produce sentence 11e m lexicalisation will proceed from the following representation provided that the mapping structure remove locking device unlock is given in the lexicon denominal verbs and wlfich will be defined later
hence in such cases it is difficult to know if the author s had good reasons to make the english and french versions so different and if the differences should be respected in the automatic generation process
the aim of this paper is to describe the main verbal differences observed in a bilingual corpus of procedural texts and to analyze their impacts on the lexicalisation mechanisnm of the sentence generation system glose NUM used in ghostwriter
we should note that these prob null main linguistic operations NUM transition from deep syntactic representation to surface syntactic representation NUM linearisation of the surface syntactic representation and NUM surface morphology
what we have said so far should be suificient to mlderstand figure NUM next page whi h represents the onstruction procedure applied to the cxamt h NUM
in this section we attempt to answer the following two questions NUM what is the d structure of french negative clauses NUM which move ments take place between d structure and s structure
from a semantic point of view it is possible to i ropose a semanti ret resentation of temporal negation and this representation matches in a way re ent results in generative syntax so that it is possible to offer a computationally realistic treatment of this interact ira without any trade off froin the linguistic point of view
as an example the diseom se 5c will receive the represenl atioil NUM the most relevant points of which being first that a temporal constant t is systematically introduced into the representation and second that negation has wide scope over event state discourse referents t remaining outside negation
but these examples are rare moreover the two last sentences of NUM involve more or less idiomatic expressions so that it seems quite reasonable to see negation here as part of a conventionalised expression he passe laisser faire denoting an event just like the verb to refuse does
since the second sentence of 5d call reasonably be thought of as introducing a state also introducing a state for the second negative sentence of 5c would lead us to lose the contrast since this would suggest for both sentences that bill was not smiling when mary looked at him
in addition to models a b and c described above the pilot experiment evaluated two other models for comparison
any sufficiently wide span whose left endword has a parent is a legal parse rooted at the eos mark figure NUM
a randomly selected set of NUM sentences was set aside for testing all models the rest were used to estimate the model parameters
l rsing which combines ajimys s of shorl er substrings into analys s of progressively longer ones
iiowever i rol ability models derived from parsers sotnetimes focus on i lci lental prope rties of the data
fhe model in i can be improved it does not aptrlr the fact that words have arities
NUM kupiec NUM merialdo NUM take the following view of row tagged sentctrce enters the worhl
model b generates a sequence of tagged words then specifies a parent or more precisely a type of parent for each word j
indeed in a typical software manual it is possible to distinguish at least three sections each with a different purpose a tutorial containing exercises for new users a series of step by step instructions for the major tasks to be accomplished and a ready reference summary of the commands
in the manual analyzed we recognized two more specific communicative purposes in the step by step section to enable the reader to perform a task and to increase the reader s knowledge about the task the way to achieve it or the properties of the system as a whole
alternatively we propose that lexical entries are descriptions of objects open to contextnal specification of their properties on the basis of constraints defined within the type system
we rthermore assume that the semantics of the predicates include a pointer to the semantics of the prepositional complements they license
to eliminate horizontal redundancy direct relations between descriptions of fully formed objects must be defined externally to the typed mulitple inheritance network or unintuitive solutions must be pursued
both the clauses and the searching procedures are mechanisms external to the inferencing mechanism that is directly related with the type system
in dealing with different complementation pattern phenomena sanfilippo constructs type system fragments where the meet of the alternative complements is defined and subtypes verbs according to complement types
attribute for to put that would be on contact in order to make sure that the locatum argument always surfaces as the direct object of the verb predicate
the same information is encoded again on a table of clauses which relate a verbal meet type with a maximal complement type and a maximal verb type
thus the marker noun verb can be inserted as a boundary marker into the input the bus goes giving the bus noun verb goes
in spoken language the translation of lengthy utterances can yield a huge amount of structural ambiguity which needs to be efficiently processed by the system
this bottom up application based on the concept of chart parsing can constrain the explosion of structural ambiguity by dealing with best only substructures using semantic distance calculations
because the selection of the best structure might have to be postponed until all possible structm es are derived the costs of translation could be high
compound noun the procedure ext lained so far is the part that the top down and bot tom up pattern application methods have in common
in contrast if structural ambiguities of substrings are always settled and are never inherited to the upper structures the explosion of structurm ambiguity could be constrained
note that a comma is not used in the input sentence because it is assumed to be a spoken language input such as the output of speech recognition
NUM NUM his sales clerk does n t understand anything NUM say and i m wondering if you wouhl help me explain what want
then the structure of the whole input string which corresponds to NUM is constructed by combining NUM with NUM
the tdmt prototype system whose domain is travel conversations is designed to achieve 2the prototype system assigns a default target expression to a surface source expression
dan jurafsky personal communication wrote an earley parser for the berkeley restaurant project berp speech understanding system that originally computed forward probabilities for restricted grammars without left corner or unit production recursion
conversely a minimal modification to the standard completion step allows the wildcard states to collect all abutting substring parses i jy rcb j k
a is the sum of all path probabilities leading up to kx y times the probability of choosing production y u
where x is a nonterminal of the grammar and are strings of nonterminals and or terminals and i and k are indices into the input string
definition NUM the probability p NUM of a path is the product of the probabilities of all rules used in the predicted states occurring in v
earley s parser can deal with any type of context free rule format even with null or c productions i.e. those that replace a nonterminal with the empty string
the probabilistic extension of earley s parser preserves the original control structure in most aspects the major exception being the collapsing of cyclic predictions and unit completions which can only make these steps more efficient
if stake can be interpreted to mean something as vague as stake as any kind of investment in any enterprise then the answer is yes
we believe the best resource is still a machine readable dictionary they have a relatively well defined set of sense tags for each word and lexical coverage is high
we found that of the word tokens which had more than NUM homograph NUM were assigned the correct homo
this approach can be further subm null m classified a supervised training where information is gathered from corpora which have already been semantically disambiguated
it is this undcrspccilication such as kgnncn and the ims ivc i i lcb for erman it malc cs rucim difforcncc whc hm the i l lcb appli s under unifica ion
accordingly no subsutnption relationship exists between the input specification of the ppm lcb as a whole and the le for kb nnen tience if tiypothesis b is assumed the lt lcb can be successfiflly blocked s however even under subsumption nothing blocks the pplr from applying to the transitive verb kaufen as discussed in section NUM therefore the grammatical sentence in NUM can be derived successfully
if hypothesis a is assumed then the cei i lcb g will be applicable to the type of i e shown for ksnnen in fig NUM since such an i l will unify with the input descrit tion of the li lcb
for gramrnar formalisms that employ the notion of unitication of attribute value structures two criteria for applicability naturally suggest themselves NUM llypolhcsis a a lexical rule applies to a lexical entry ifr the lexi al entry unifies with the left hand side of t he lexical rule
l ol impersonal passives the comps list of any transitive verb whose leftmost complement is marked by genitive or dative case remains unchanged while the singleton list of the subject value of the active form becomes the empty list in the i e for the passive form
it is important to distinguish two tasks that need to be performed in computing with lexical rules NUM NUM the algorithm that decides for a given lexicai entry whether a lexical rule is applicable to it and NUM the algorithm that computes for a given lex null ical entry the output specification of the lex ical rule i.e. the derived lexical entry
the slasii wdue in fig NUM is percolated h om the non terminal node for the verb ka ufl n by the nonlocal i eaturc principle to the sister node of the topicalized constitueut des buck fhe top local tree is licensed by the llead filler ii schema which binds oil the slasii wdue so that the sentence node has an empty si ash set deg
this is due to tile silnple fact that certain constraints have een elinfinated in the subgranunars
if so and if it occurs at the beginning of a sentence reject it again
regular expressions can catch several of those cases but it is difficult to get certainty e.g.
we lid not tmrsue this strategy since it introduces an additional NUM recessing step during parsing
this consists of removing the words before and or after the first proper noun in the match
in the following we discuss possible options and problems for the distribution of information in a cospecifying grammar
in fact our measurements indicate that syntax in our grammar provides most of the genuine constraints
for speech parsing the nodes represent points of times and edges represent word hypotheses paths in the word lattice
this narrows the space of successflfl hypotheses on the syn parser s side see remarks in section NUM NUM NUM
tile sf m parser must be able to report back to the syn parser at least when its hypotheses failed
an important consideration then is that the overhead for this communication should not outweigh the gains of distributed processing
the second complication results from a basic limitation in treebank parses there is no distinction between arguments and adjuncts
an example of this is given in figure NUM the parse tree for the sentence in NUM
the pp containing the vpe must be eliminated leaving the correct antecedent nourished mine
to illustrate these criteria we give three examples one for each success criterion
NUM this is implemented by multiplying the vp by our standard penalty value of NUM
without the application of the quote preference the system incorrectly selects convulsed mr gorboduc
computational linguistics volume NUM number NUM here the correct antecedent is be ideal
without this constraint the vp outgrew the vax is incorrectly selected by the system
there is a preference for a similar base form of auxiliary in antecedent and vpe
we prefer an antecedent that shares the same category of auxiliary form as the vpe
we describe the development of a generator for erman built by reusing and adapting existing linguistic data and software
l he arguments of the main verb are generated lexicon driven once the lexical head of the phrase has been established
to account for vi and v2 phenomena a mechanism resembling the gb notion of head movement is imple null mented
also some of the phrase structure information generalized in the form of principles could be compiled into phrase structure rules
thus the structures have considerably been fiattened and some aspects most notably subcat and content have beet encoded differently
the representation of phrasal signs in iipsg parallels the one of lexical signs an additional feature dtr s carries the subconstituents o the phrase
designers of stochastic parsers seem to have given up on the problem of creating a statistically adequate theory concerning parsing unknown events
a typical corpus based algorithm constructs a training set from all contexts of a polysemous word w in the corpus and uses it to learn a classifier that maps instances of w each supplied with its context into the senses
the interchanged data between components a component normmly corresponds to a unique software module is very heterogeneous with regard to both type and quantity speech information as it is sent from the recorder to the speech recognizer consists of a stream of short integer values which may amount to several megabytes
i know you re busy but might i ask you if em if you happen to have an extra pen that i could you know eh maybe borrow
there are a number of factors that need to be considered in trying to select the most appropriate example in the database for the given input
model x did the same n gram tagging as models a and b NUM for the preliminary experiment rather than n NUM but did not assign any links
when a word stochastically chooses one set of requirements on its parents and children it is choosing what a link grammarian would call a disjuuct set of selectional preferences for the word
finally we point to experimental resul s that compare the three hypotheses parsing performance on sentences fi om the wall b treel dourhal
he mighl guess that each substa ing mmysis shottld bc t lcxicm tree tagged he ulword plus ajl icxical sulfl rees dependc nt upon i
model b taking the markov process to generate tag word pairs from right to left we let NUM define the score of a span from word k to word
some sentences generated in this way are illegal because their disjuncts can not be simultaneously satisfied as in model a these sentences are said to be removed fi om the population and the probabilities renormalized
furthermore there are a number of fully or semi lexicalized morpheme sequences that carry specific illocutionary forces but that are not totally predictable from its forms
positive face or involvement concerns one s desire to be liked by others to be involved with others and to be part of the same group
secondly it is not an answer to q1 because of its incompetence to discriminate irony from other non literal utterances e.g. a lie in which the maxim of quality is tlouted
on the other hand all the utterances of figure NUM are ironic because they implicitly express the three comt onents of ironic enviromnent as we will show in sections a a a s
also a proi osition p expressing the claim that candy eats the pizza is written as t eat x a rcb
ill the sanle way 2b satisfies condil ion NUM since its oiltent NUM j ch a n up y a is identical to a
alter a while she discovered that his room is still messy and said to her son NUM NUM i love children who keep their rooms clean in leed
this paper argues that stylistically and pragmatically high quality spoken language translation requires the transfer of pragmatic information at an abstract level of utterance strategies
to a certain extent the apparent heterogeneity of the emall undermines the results of any similarity measures applied to this corpus
loglikefihood appears to be the more principled of the two measures and it is suggested that this offers the greater potential
NUM we found that the best results are obtained within three iterations
in general trajficking and crime need not be similar of course
one important future research goal is tile in orporation of incremental n orphologieal analysis and generation into the prot osed translation strategy which would provide a sinmltaneous interpretation mechanism tbr n plication to a t ra ti al spoken lm guage translation system
in x at f for the substring goes to chinatown at ten a m combined with NUM and NUM the variables x and y are substituted for the compound expressions goes to chinatown and ten a m respectively
when the rightmost word has been processed the derivation of the passive arc of the whole input gives the parsed result in our example the derived process of the passive arc NUM which is the combination of NUM and NUM
if the retrieved pattern is of the type x a y and a left neighboring passive arc can satisfy the condition for x s instantiation create an active arc for x a f in which y has not yet been instantiated
in contrast the incremental method determines the best structure locally and an constrain the number of competing structures fbr the whole input by performing b in l arallel with c consequently translation costs are reduced
retrieved pattern linguistic level the x ompound noun x noun verb y simple sentence x to y verb phrase noun phrase x at y verb phrase noun phrase x a ra
for instance in the noun phrase x of f the variables x and y can not be instantiated by a simple sentence but can be instatiated by a noun phrase a compound noun and so on
thus NUM has two possible structures by the application of x at f x to f at the verb phrase level and x a m at the compound noun level are also applied
however the top down and breadth first translation strategy in the earlier versions of tdmt which yields a quick response for inputs with restricted lengths may show poor efficiency when processing a very lengthy input or inputs having many competing structures
moreover the analysis can not deal with bare ellipsis cases like NUM where a bare adjunct fragment does not correspond to any constituent in the antecedent clause
others subcat comp dtrs h eomp dtrs l adj dtrs phon ltoo
the algorithm is a generalized procedure for syntactic reconstruction which provides a unified way of handling a significant variety of ellipsis constructions
the algorithm will identify sings as the head v of the antecedent clause and substitute it for the empty v in NUM
the elided conjunct in 15b is ill lbrmed because the reciprocal np each other in the bare argument is interpreted as illicitly bound from outside of its local syntactic domain
13a a a2 book reviews last year bj b2 articles this year b s l wrote book reviews for the journal during last year bill c
NUM concatenate arg list and adj list to create a combined list ph list of the phrasal arguments and adjuncts of a
e john sang but not in new york at the concert for three hours on tuesday to impress his music teacher
ldega large library of finite state functions is available at xerox
consider the following example involving sloppy identity in vp ellipsis NUM tom1 loves his1 cat
we have already seen how the sloppy reading is derived for st and for NUM
centering theory posits a discourse center a distinguished discourse entity that is the topic of a discourse
non ironic echo utterances do not include pragmatic insincerity and or do not irnplicitly communicate the speaker s attitude
several different implementations of horizontal relations exist
can be distributed among independent surface strings
we use ipsg to model our approach
NUM mary stuffed the pillow with feathers
mentals center for the study of language and information
cont tct optional obligatory oll go on con opt
for the construction of the vp a simple rule
null NUM lexical rules what are they
table NUM presents the set of words corresponding to the genotype nfp v2s and their resolution with respect to lexicm frequencies and genotype frequencies
in a way similar to decision trees table NUM shows how the use of context allows for better disambiguation of genotype
NUM french words and morphological variants to illustrate our position we consider the case of french a typical romance language
table NUM shows how costs are computed for a specific bigram and how these costs are used to make a tagging decision
tag probabilities in this class are approximated by tags of words that appear only once in the training corpus
to ensure a correct concatenation of initial and middle subsequences we formulate a concatenation constraint for the classes
the main advantage of transforming an hmm is that the resulting transducer can be handled by finite state calculus
of this data has an impact on the tagging accuracy of both the hmm itself and the derived transducer
the next tag is selected on its transition probability given the first tag and its class probability etc
NUM chemin de maupertuis NUM meylan france andre kempe c grenoble rxrc
since all transducers are approximations of hmms they give a lower tagging accuracy than the corresponding hmms
at this point the tagger has read and stored the words of a whole sentence fig
the tagging speed of the transducers is up to five times higher than that of the underlying hmm
most transducers in table NUM are faster then the underlying hmm the n0 type transducer about five times s
NUM lexicalise the remaining arguments xa xn and link the resulting lexemic structures to NUM NUM
however actions may have attributes manner temporal constraints s 5e gain access to rear compartment
on the former metric our model can generate translation lexicons with precision and recall both exceeding NUM as well as dictionary sized translation lexicons that are over NUM correct
the replenish of the accumulator should cause the warning light to come on on the hydraulic panel
the lexicalisation rules defined so far perform mappings between a single concept the predicate and one or several lexemes
rl simple verb construction NUM look in the concept lexeme mapping structures for a correspondence p v
several remarks should be made about these rules to link predicative lexemes to their depen null dents i.e.
we will ignore these communicative constraints in this paper because they are of minor importance for the linguistic phenomena considered here
when the hidden parameters are conditioned on different link classes the estimation method does not change it is just repeated for each link class
both simplified english and rationalised french include a writing rule which says that specific words should be prefered over general words
such verbs ensure conciseness and sometimes the lack of denominal verbs in french makes the french version much longer
the information following the final is optional this information was previously hand added to the assigned thematic grids
thematic roles with unspecified prepositions null NUM example we parked the car near the store
if a preposition occurs in the grid but there is no matching primitive representation the preposition is considered to be a
the default position of the marker is the left most occurrence of the lcs node corresponding to a particula r
the logical head is represented as a primitive field colnbination e.g. goident is represented as go ident
zan underscore designates an obligatory role and a comma designates an optional role
in the lcs framework thematic roles provide semantic information about properties of the argument and modifier structures
figure NUM the feature structure subsumption analysis of the ungrammatical NUM
the comparative computational complexity of both the unification based approach and the lcg accounts is also of interest
now consider the related construction in NUM involving conjoined predicates as well conjoined arguments
finally we assume the standard lcg introduction and elimination rules for the directed implication operators
the standard account of adverbial modification in standard lcg for instance treat
first the marked words rcb rm cerl aiu areas connect d nodes fwords in tlmt hesaurus
ing sections up to the conclusion discusses an important difference between the two approaches
the nature of an appropriate feature system for lcg is still an open question
when a thesaurus is used for nli applications such as a n information retrlewd a nd disam
words with strong similarities but whose relatioashit s seem st r nge to huma n intuitioa reduce the a ceuracy of the proposed method
howew r most existing thesauruses are compiled by hand and eonsequently the following three problems occur when they are used for ni p systems
towew r the relat ionshi NUM heavy oil and gas i suggests the position of heavy oil
in this case fly sub is a viewpoint for the node airpla n which is the topmost of the connected nodes
yhe main goal of our work is to expand the thesaurus automatically explicitly including distinguishing features viewpoints and to construct a domain setlsitive thesaurus system
however in the osv sentences the object not the subject is most often the cb of the utterance
from these constraints of the ontology we can obtain the constraint of persons as follows
NUM if c c push es this button then ca will come out
f specially subjects are omitted very o1 ten
we can use it to analyze sentences based on our proposal
it becomes more assurnlstive when the subordinate clause shows a state
we check constraint NUM and our estimati m of to
if there is no need of cp please discard cp
our estimation about to has been already confirmed in section NUM NUM
the speaker uses the sentences to prompt hearers to do an action described by the sentence
NUM if NUM fails choose the first item in the cf list i.e. the subject as the discourse new topic
we have shown that although parse results are considerably lower on unedited data than on cleaned up data they are very competitive if not better than other models
NUM NUM enriching dop3 with a dictionary the model dop4 a method which may further improve the accuracy of dop3 may lie in the use of an external dictionary
much work in statistics is concerned with the fitting of particular distributions to sample data with or without motivating why these distributions might be expected to be suitable
in this section we study what is involved in creating an extension of dop1 which can compute probabilities of parses containing unknown category words
besides the qualitative aspect the filter can also affect the quantitative aspect by collapsing or expanding certain entries e.g. the 1st and 2nd person singular of many verbs constitute the same entry in the database but are differentiated afterwards or excluding specific combinations after examination of the input
e.g. if a simple form of the verb hebben have appears the auxill ary reading is kept only if a past particl ple is present in the context NUM awe only consider the syntactic context
NUM o avoid infinite loops repeatedly firing the same rule that is not able to alter the current set of morphological readings the same ruleset can only be fired once for the word on the same position during the same pass
the extensive tagset tagsetl provides all the morphosyntaetic information as required by the dmlp parser for sentence analysis while the reduced tagset tagset2 consists of NUM ategories and NUM speciliers which gives NUM meaningfifl combinations
NUM we conclude that we can not make a decision
we desert b c our experimental rcsull s ill th is section
a number of thesauri based on these data using our method
rcb while the description length has decreased during the past NUM
itere t is the a nnealing t enq crat urc
our method based on mdl resolves this issue in a unified fashion
multi tape two level morphology is an extension to standard two level morphology where more than one lexical tape is allowed
the remaining measures are derived from the base template by afiqxation they have no templates of their own
topicalization was also used in other cases where the system must shift the focus of attention to the location already described in the preceding discourse
this model incrementally produces utterantes to propose the solution of a given problem while simultaneously solving the problem in a stepwise manner
there are NUM cases where the satellite describes a precondition of a domain action which amounts to NUM of all cases
schema consists of an action description appli cability constraints and an effe t NUM it defines a communicative action
we analyzed the discourse structure in a corpus of task oriented diah gues which were collected by the folh wing method
so global inheritance can be seen as essentially the same mechanism as local inheritance but layered on top of it following global links alters the local context too but not vice versa
problem rate shows the real myopia of the test
a computational model of incremental utterance production in task oriented dialogues
about NUM NUM words NUM sentences of test data were utilized
first is exact match with any correct parse listed for the sentence
for instance the context firstname x lastname e.g.
table NUM comparison of ibm manu s and atr lancaster general english treebanks
we can ask about the tag of any word in the source treebank parse
figure NUM ibm lancaster treebank and atl lancaster parses for same sentence
of course the results here do not include models used in tagging
hence the order chosen determines what information is available to each model
system design the atr parser is a probabilistic parser which uses decision tree models
a pp may be attached to the preceding np and form part of a single large name as in np midwest center pp for np computer research
as a result there occur in nominator s output certain names such as american television communications and houston industries inc or dallas s mcorp and first republicbank and houston s first city bancorp of tezas
thus if in one document president clinton was a variant of william clinton while in another document governor clinton was a variant of william clinton both are treated as variants of an aggregated william clinton group
after information about names and their referents has been extracted from individual documents an aggregation process combines the names collected from all the documents into a dictionary or database of names representative of the document collection
paris usually refers to the capital of france rather than a city in texas or the trojan prince but in a particular context such as a discussion of greek mythology the presumed referent changes
text processing applications such as machine translation systems information retrieval systems or natural language understanding systems need to identify multi word expressions that refer to proper names of people organizations places laws and other entities
in its minimal use of resources nominator follows this trend it relies on no syntactic information and on a small semantic lexicon an authority list which could easily be modified to include information about new domains
in order to determine whether splitting should occur a name sequence containing an ambiguous operator is divided into three segments the operator the substring to its left and the substring to its right
thus jordan hills person from one document is aggregated with jordan hills per son from another where there was sufficient evidence such as mr hills to make a firmer decision
reports are an organized synthesis of data that span a whole array of forms going from tables of numbers to a text summarizing the findings
1deg given this evidence it is very important for the icicle system to have the ability to generate text at or near the user s current second language proficiency
in the first sample the bare form of the verb should appear after the infinitive marker to instead the verb appears to be inflected with tense
during the system processing information about the student s language usage over time as well as user system interactions can be tracked and updated through the history module
the error identification component may also contain semantic rules and discourse information that could add annotations for the response generator though these are beyond the scope of this paper
the system would analyze these texts for errors engage the student in a corrective tutorial dialogue and offer possible corrected versions for some of the original input sentences
in fact the set of errors a given student makes may be influenced by the l NUM but that set changes over time as the l2 is acquired
in particular one would expect features shared in the l1 and l2 to be acquired more quickly than those which ale not due to positive language transfer
this linguistic model is based on two components a model of the user s first language in our case asl and a model capturing the user s progress in acquiring the second language
a major proposal of our work is that a model incorporating possible effects of the first language should be included in the component that is responsible for identifying errors in the production of a second language
for example generally a beginning student does not attempt to use a sentence that contains a complex sentential complment or a relative clause until after he she has mastered the use of simple subject verb object sentences
compare with other optimization or con null straint satisfaction teehlfiques applied to nlp tasks
results obtained by the baseline tuggers are found in table NUM
we have also available ternary constraints extracted from trigram occurrences
a weighted labeling is a weight assignation for each possibh label of each variable
update the weight of each variable label ac ording to the support obtained
null second row of table NUM shows the results obtained when using binary plus hand written constraints
all we need is to state tile onslxaints between senses of neighbor words
we tried several candidates to represent compatibility mutual information association ratio and relative entropy
see section NUM for further details drawbacks of tire algorithm are its cost
the decision of whether a cue phrase is considered to have a discourse usage is sometimes based on the context in which that phrase occurs i.e. it depends on the occurrence of other cue phrases
item a representation of all of its sy stematically related senses from which fuxther semantic processing steps can derive discourse dependent interpretations
this involves a similarity measure between the linguistic contexts of classes of nouns that are in corelex and the linguistic context of unknown nouns
rather we compute precision and recall on the classification of those nouns that are in corelex because we can check their class automatically
the assumption then is that the precision and recall figures for the classification of nouns that are known correspond to those that are unknown
additionally it was shown that coi lex provides for more consistent assignments of lexical semantic structure among classes of lexical items
finally the approach described above allows one to generate domain specific semantic lexicons by enhancing corelex lexical entries with corpus based information
the measure NUM conceptual distance among concepts we are looking for should be scnsflive io the length of the shortest palh that connects lhe concepts involved
while these measures have led to significant advances for knowledge lean applications they do not adequately motivate progress in computational semantics leading to the development of large scale general purpose nlp systems
a purely corpus based statisticm approach to nlp on the other hand has an extremely narrow range of knowledge but may haw a large size
we propose an operationalization of this measure and show how to characterize an nlp system along the dimensions of size corpus coverage and depth
although statistical methods have been shown to work on some problenm and applications they are typically applied to one or two phenomena at a time
mixed strategy ni i systems are epitomized by i angloss 199d a multi engine translation system in which semantic processing is only one of the possible translation engines
figure NUM shows the approximate position of several well known approaches and systems including a possible cyc based system in the 3dimensional space of semantic coverage
the big question for this kind of system is whether it is in fact possible to acquire knowledge without a reference to an intended application
the developlnent of natural language processing systems is currently driven to a large extent by measures of knowledgebase size and coverage of individual phenomena relative to a corpus
poor depth suggests that knowledge and processing techniques are either application or languagespecific and limits the ultimate potential of the system in solving semantic problems
consequently global descriptors are also distributed through the local inheritance network and so are implicitly present at many node path pairs in addition to those they are explicitly defined for
but if the entries represented the phonology of the words in datr also then these predicates could be defined on the basis of the feature composition of the stem final segment
let s consider the following japanese sentence which shows a certain instrnction
if we seek to establish the mor form of word3 we are sent up the hierarchy of nodes first to sew then to en verb and then to verb
for the purposes of this section where we need to be particularly clear about this distinction we shall call a sentence containing just a single statement a simple sentence
computational linguistics volume NUM number NUM mor present tense sing one am mor present tense sing three is mor present tense plur are mor past tense sing one mor past tense sing three mor past tense sing three was mor past tense plur were
the first is really just a syntactic trick if the path on the right hand side is the same as the path on the left hand side it can be omitted
nevertheless the notion of global inheritance does have a declarative reading very similar to local inheritance but as we have already suggested layered on top of it
syntactically the distinction is made with the equality operator for extensional statements as above we use while for definitional statements we use
so in a sense we do want all the above statements to be present in our description what we want to avoid is repeated specification of the common elements
since the number of parameters in a multi dimensional joint distribution is exponential in general it is infeasible to accurately estimate them in practice
dependency is meant the relation that exists between case frame slots which constrains the possible values assumed by each of those slots
in addition the verbm complex serves as the domain over which auxilim ics c u t i ronl ed
note that the case specifications on the left hand side of the rules m e crucial since they condition which classes of transitive verbs appear in personal and impersonal passives
viewed procedurally the ppi i lcb is meant to ap ply to es for transitiw verbs such as kau en as shown in ig
locali icomp phrase nonlocai iinii er slasti j localicativalicomps
this restriction is necessary to rule out sentences such as NUM in which a single lexicm item i.e. a word in terms of the type hierarchy of hpsg is t opiealized
in impersonal passives a datiw or genitive np complement of a transitive verb e.g. the dative np dem mann in NUM exhibits the same case assignment in the active and passive forms
the subsumption requirement for lr application is based on the insight that lrs should apply only to les that are instantiated at least to the extent that the input description of the lr minimally requires
however this is not problematic since the lexical rule application corresponding to such a transition will simply fail at run time
with respect to this base lexical entry we fine tune the finite state automaton representing global lexical rule interaction by pruning transitions
this conflicts with the standard assumption made in hpsg that only the properties changed by a lexical rule need be mentioned
NUM see section NUM for a more detailed discussion of the relation between our approach and this perspective on lexical rules
though this predicate represents what was explicitly specified in the lexical rule it does not accomplish exactly what is intended
accuracy is the expected rate of agreemnt between a treeb lcer and the grammarian on parsing a given sentence in a given document of test data
NUM NUM except that test data consists of atr treebank format documents of which we also possess aligned source treebank in this case ibm lancaster treebank versions
evaluation methodology we evaluate trsebank conversion to atr treebank format in the same way as we evaluate the parser when it is trained in the normal ma ner cf
f re NUM shows a parse for a sample sentence first from the ibm lancaster treeb k and next from the atr lancaster treebank
the language provides facilities for navigating a parse tree determining feature values of a given node and m hng simple boolean or arithmetic computations
NUM together the bracket locations rule names and lexical tags of a treebank parse specify a unique parse within the gr rnra r
the proposed solution was to check whether the vpe has a sister node that is a prepositional phrase or noun phrase
the portion of the penn treebank examined the brown corpus about a million words contains about NUM vpes
the paper will establish the current status of the algorithm based on this automatic evaluation categorizing current problem situations
testing the program s performance involved finding the percentage of correct antecedents found by any or all of these algorithms
fortunately such situations seem rare as none were found in the brown corpus
these results imply that the structure of a sentence alone is insufficient to detect subdeletion
vpeal selected the correct verb to head the antecedent but the selected antecedent was either incomplete or included incorrect information
NUM check if there are any prepositional phrases or noun phrases that are sister nodes to the vpe head verb
when the algorithm was implemented however the number of correct answers improved to NUM an increase of NUM
furthermore to automatically evaluate the algorithm utilities were developed to automatically test the output of vpeal for correctness
therefore the choice of the production x a is not part of the outer probability associated with a state kx a
before going into the details of computing outer probabilities we describe their use in obtaining the expected rule counts needed for the e step in grammar estimation
full dist ribut odnoss a n obviously bo ol i aine i i y cxl rossing evory i ieoe ot in l rtna
9a linguistic expression realizes a semantic content when the former directly rcfers to the latter or the situation described by the former involves the latter
NUM NUM satellite act if on mother pass elaboration then act class elaboration if on mother pass purpose then act class purpose if on mother pass reason then act class reason if on mother pass background then act class background
act act act act inform background inform reason i ii ii ii i i went to he d agreed to it took us as we dissee fred
this suggests in turn the value of the particular set of concepts incorporated into the cardiff grammar and of the grammar writing tool defrel in which both genesys and genedis are written
o t finally it leads on to choices m themauzm the s and to the realization rules that proviffe for re entry like those for the s in a move
in rst on the other hand there is the potential for the unlimited recursion of structures consisting of a nucleus with a satellite in the relation of elaboration or reason etc
in some cases the probabilities are absolute as in our case where the sp rule on elaboration makes it NUM certain that the s will follow
we enter it at discourse unit and find that the probabilities in the initial system are set NUM to move
the finaveffect of selecting withsatellite m is to provide that the network will be reentered to fill s as in rules NUM NUM and NUM NUM
we think the preliminary results of our system in this paper are promising since incorporating the information of semantic constraints and the global structure of discourses will improve the performance
partitioning might cause that the information in the previous and current post partitioned simple sentences does not inchide even tile information in the current prepartitioned sentence
llsed to avoid rel ating iiollll t hrasc thai al tmar d in NUM he previous s htt cltc ks
in this case also the center of the current sentence is computed similarly to the ordinary algorithm and the antecedent of the zero pronoun becomes the cb of tile current sentence
in addition to handle complex sentences he adopts the other approach where they are treated as a single unit and admits that some problems arise because of this approach
first the intrasentential ellipsis that the antecedent exists in the same sentence NUM can not be handled with the centering theory because the centering theory only handles the intersentential ellipsis
a zcl o i l ii llll c rcb tll l t xmsidei cd as a noun hra se
in order to motivate our restrictions we proceed in three steps
kay gawron and norvig NUM for details
not possible to represent NUM in an adequate way
the second class of approaches achieves tractability by restricted representation languages
this problem within lexical disambiguation were directly adopted from knowledge representation
this procedure would exchlde e.g. for einige arzte habe n
first order case seems nevertheless representative since we have to teal with the decidability problem
but note that this does not allow us to test whether q5 is valid or not
the french english software manuals were provided by gary adams of sun microsystems laboratories
this model will then be tailored to the needs of individual students via a series of filters one for each user characteristic that might alter the initial generic model
in developing the language learning model and its filters we plan to compare our initial model derived from acquisition literature with the writing samples that we have already collected
the first is determining which errors to respond to in detail and the second deals with the kind of english syntactic constructions to use in the realization of the response
essentially we cfin view the placement in slalom as highlighting language features and corresponding mal rules that we expect the user to be using at a given point in time
given that transfer has been documented between spoken languages it is reasonable to ask whether or not language transfer could occur between asl a visual gestural language and written english
the term language transfer generally refers to the influence that knowledge of one language li has on the production and or comprehension of a second language l2
possible correction have side effects a third way languages may differ is in regard to requiring morphological changes or additional lexical items for strictly syntactic reasons
to summarize part of our model of the learner s generation process includes a language model which captures the influence of the first language on the production of the second language
it first does a syntactic parse of the sentence using an english grammar augmented with error production rules called mal rules s1e82 wvj78
while an error might be recognized doing a syntactic parse of the sentence in order for beneficial correction both the specific error and its probable source should be identified
the performance of the japanese fastus together with other japanese systems demonstrated that the basic information extraction ie technology was portable to a language very different from english
we plan to fully customize the dictionary for ie purposes and augment the system with coreference resolution and compile time transformation capabilities demonstrated in the english muc NUM fastus NUM
the second japanese fastus called mimi for ears in japanese summarized spontaneous human human dialogues and was developed during NUM NUM
after the name recognizer phase the alias recognition routine recognizes some of the unknown words as aliases of the organization names recognized earlier in the same document
the document object is input into the sentence loop consisting of a sequence of finite state transducers namely the preprocessor name recognizer parser and combiner
these juman morphemes are turned into fastus lexical item objects with slots for literal string normalized string lexical category inflection type and so forth
this is not problematic as long as this nondeterminism will eventually disappear e.g. by combining these solutions with the solutions to cat NUM
the higher ranked nocoda constraint outputs NUM marks while align prefix outputs t marks
for the tagalog example the output rankings for the candidates are shown in figure NUM
this beconres extremely relevant for larger scale resources
for other languages similar system exist
NUM NUM two level morphology tlm
quants and the psoa are copied
the corpus investigation guided the grantmar developnrent
the change has been done for merely esthetic reasons
methods for large scale grammar development
we will introduce this component by way of exemplification
this also holds in cases like gib st
it is used to derive from the guard for the original rule a guard for the rules defining the first right hand side literal
a variety of problems regarded as standard in computational linguistics such as quantification reference and the like are thus ignored
our probability model is instead shaped by the key discourse problem of the atis domain which is the inheritance of constraints from context
the surviving theories are then combined with the conditional word probabilities p w i t computed during the parsing model
finally evaluating on a common corpus makes it easy to compare the performance of the system with those based on different approaches
in parsing for example it is sufficient to provide the system with examples specifying the correct parses for a set of training examples
the focus of this work is primarily to extract sufficient information from each utterance to give an appropriate response to a user s request
directly modeling p mo i w is difficult because the gap that the model must span is large
within each module every processing step is assigned a probability value and very large numbers of alternative theories are pursued in parallel
the overall structure of our approach is conventional consisting of a parser a semantic interpreter and a discourse module
work on the system is ongoing however and interested parties are encouraged to contact the authors for more recent results
complex wordforms nor idiomatic expressions are yet handled in a conclusive manner
mor form mor quot syn form
but for word2 mor form inherits from mor passive participle
mor past participle done mor present tense sing three does
evans and gazdar lexical knowledge representation theory query value
the final task is that of theory induction
the simplevalued definitional statements thereby defined map directly to extensional statements and global inheritance uses the global inheritance statements now distributed to further distribute these extensional statements about simple values
so far in this paper we have almost entirely ignored the distinction we established between definitional and extensional statements but with this declarative reading of global inheritance we can do so no longer
finally it is worth reiterating that datr descriptions correspond to sets of statements the order of sentences or of definitions within a compound sentence is immaterial to the relationships described
we can see that words belonging to the same genotype are likely to be tagged with the same tag for example the genotype nfs vis v2s v3s is tagged as nfs
also the refinement that is contmned in our system reflects the real morphological ambiguities due to the rich nature of the morphological output and the choice of tags
using these tools we have created a set of programs which generate finite state transducers from descriptions of linguistic rules in the form of negative constraints and for encoding distribution information obtained through statistical learning
out of the NUM words listed in the table NUM NUM words marked unseen in the table could not be estimated using lexical frequencies alone since they do not appear in the training corpus
it takes the text as input and produces an fst that encodes each possible tagging of the input text as one distinct path from the start state to the final state
the contraint based tagger is proven to have better performance than the statistical one since rule writing is more handlable or more controllable than adjusting the parameters of the statistical tagger
in this case we know that the jmp nmp genotype has a right context consisting of the genotype p r 4th column 5th fine
as explained in section NUM NUM out of a trmning corpus of NUM NUM tokens we extracted a total of NUM unigram genotypes NUM bigram genotypes and NUM NUM trigram genotypes with their respective decisions
the more elaborate one includes a human check on certain items which not only gives more exact information on the test result but in particular shows the quality of the regular test
if yn and ny are both relatively high this shows that there are structures on which a is better than b and vice versa the systems complement each other
for nn the distril ution is the same with the opposite probabilities a binomial variable for test size nand p NUM pa NUM pb
this means that we can safely conclude that b really performs better than a the real test is a lot smmler only NUM bracket pairs but that is still enough to be meaningful
we will later put this to more use but for now we just use it to conclude that yy is expected to be around NUM and nn is expected to be around NUM
we suggest a more detailed method for testing a parsing system using constituent boundaries with a number of measures that give more information than current measures and evaluate the quality of the test
particular problems we look at are arbitrary choices in the treebank errors in the treebank types of errors made by parsers and the statistical significance of differences in test scores by parsers
the only result of evaluating these brackets would be a few errors in the treebank which is often not really worth the trouble unless the treebank is suspected to contain many errors
guessing the real test size the idea behind this method is that some brackets are almost always produced and some are never and those should be discarded so the real test remains
we decided on a certain level to permit brackets and the tree from the treebank also stopped at some level so that remaining more precise bracket pairs were amongst those counted as spurious
for example the column NUM means that a target word is assigned to up to NUM class codes
appropriate word classes for a new word are identified based on the probability that the word belongs to different word classes
in the case of a compound noun the noun was transformed into the maximal leftmost string contained in bgh NUM
this approach aims at compensating the sparseness of co occurrence data by using existing linguistic knowledge such as wordnet
among these tuples only those which include the postposition wo typically marking accusative case were used
unlike bottom up parsers it also computes accurate prefix probabilities incrementally while scanning its input along with the usual substring inside probabilities
proof entry x y in the left corner matrix pl is the probability of generating y as the immediately succeeding left corner below x
jerry feldman terry regier jonathan segal kevin thompson and the anonymous reviewers provided valuable comments for improving content and presentation
this highly ambiguous grammar generates strings of any number of a s using all possible binary parse trees over the given number of terminals
interestingly a scfg may be inconsistent and still have converging left corner and or unit production matrices i.e. consistency is a stronger constraint
on a test corpus this technique cut the number of generated predictions to almost one fourth and speeded up parsing by a factor of NUM NUM
the parsing functionality arises because the generator keeps track of all possible derivations that are consistent with the input string up to a certain point
in the following sections we assume that no null productions have to be dealt with and then summarize the necessary changes in section NUM NUM
the new r z gl y factor in the updated forward probability accounts for the sum of all path probabilities linking z to y
the nodes labeled by c represent s when she wants to communicate c and those labeled by m represent r when she wants to interpret mi for i NUM NUM the inference by r when interpreting message mi is a similar tree rooted by mi
in the above example the global solution which involves saying the man was angry with him and interpreting it as angry fred max maximizes the utility from the sentence level game but not from the np level games
in typical applications of signming game t m and a are not discrete sets as in the above example but connected subsets of real numbers and s s preference for r s action is the same irrespective of her type
1deg for instance for instance after an utterance of a house the door realizes the house referred to by a house 1degthis is not a game tree but a tree of belief embedding
this game might involve other possible associations such as that between angry max fred and the man made him angry but as mentioned at the end of section NUM contents and messages other than included in figure NUM probably accompany great costs and hence may be neglected
grice s original notion of nonnatural meaning further entails s s intention of r s believing when e is a proposition or a reference or obeying when it is an order or a request c but we disregard this aspect and concentrate on this core
a meaning game addresses a turn of communication cs m c which stands for a course of events where s intending to communicate a semantic content ks sends a message m to r and r interprets m as meaning cr
this preference is accounted for by the meaning game as shown in figure NUM in this probability NUM p2 game fred and max are semantic contents and he and the man are messages
this set of optionally inserted brackets equally does not contain those which potentially could be used for the replacement of adjacent non empty strings i.e. aune on the left and aune on the right side of the expression
rcb leftcontext o rightcontext o replace b right oriented lcb u1 li ii h rl rcb NUM righteontext o replace
we rive it from the left context constraint by reversing every right context r before making the single constraints i not pi and revel sing again the result after having intersected all h
a NUM x ii NUM that maps the string bb only to xbxbx since bb is here considered exclusively as a result of the concatenation c b c b
is collected togel her m a batch and performed m parallel at the same time in a way that all of them work on the same input i.e. not one applies to the output of another replacement
au i i d azzn NUM i x i NUM x li l u
the brackets 2e t le i le i NUM and NUM re i le 2el in NUM are inserted on the lower side any number of times including zero i.e.
rn which models a linguistic derivation by rewrite rules the upper side of the first relation r1 contains the underlying lexical form while the lower side of the last relation rn contains the resulting surface form
suppose that the meaning of o nlg is determined by the following rule
inherit its fsv fl om its source clause by unification
in this way it avoids some of the pitfalls these theories encounter
the inclusion method also fails since no model for angioplastie f includes a concept compatible with segment ii and no model for segment ii f includes a concept compatible with angioplasty
we therefore experimented how this domain model could help semantic analysis to go from the flexibility of natural language to a constrained conceptual representation a typical problem encountered being metonymy
lexical probabilities are acquired for both basic and derived lexical entries independently of the lexical rules used to create derived entries so a derived entry might be more frequent than a basic one
our approach assumes that attested uses of derived words and senses are explicitly recorded but that productive use of lexical rules is also possible though controlled by probabilities associated with rule application
see for example figure NUM which is intended to cover the noun and verb lacquer plus the derived form lacquerer with agentive and instrument readings taken as distinct
predicates map to conceptual graphs most of them are reduced to one concept since most of the words in the lexicon are technical terms for which a type exists
the right stuj b in the case of at least one county primary school they were offered with perfect timing saute potatoes carrots runner beans and roast cow
we excluded entries made up of more than one word such as square dance and also pruned the set of nouns to exclude cases where a non derived verb form would confuse the results e.g.
the treatment proposed here is one of many possible schemes for estimating the productivity of lexical rules and integrating these estimates with the estimation of the probabilities of unseen entries for given word forms
in some cases a combination of large corpora and sense taxonomies can be used to provide a rough estimate of lexical rule productivity suitable for instantiating the formulae given in the previous section
by using finitestate transducers it is furthermore possible to use a bit more expressive rule formalism including for instance the kleene operator so that one can use a much smaller set of rules to cover the same set of local linguistic phenomena
for every remaining ambiguous token with unambiguous immediate left and right contexts i.e. the tokens in the immediate left and right are unambiguous we perform the following by ignoring the root stem feature of he parses NUM
the idea of voting can further be extended to a path voting framework where rules vote on paths containing sequences of matching parses and the path from the start state to the final stated with the highest votes received is then selected
the last four columns in this table present results for different values for the parameter rn mentioned above m NUM denoting the case when only the highest scoring parse s is are selected
we present a constraint based morphological disambiguation system in which individual constraints vote on matching morphological parses and disambiguation of all the tokens in a sentence is performed at the end by selecting parses that receive the highest votes
if uk and vk co occur much more often than expected by chance then any reasonable model will deem them likely to be mutual translations
we prefer not to model those cases in order to achieve higher accuracy with less effort on the cases where the assumption is true
unlike other translation models the word to word model can automatically produce dictionary sized translation lexicons and it can do so with over NUM accuracy
thus the argument between the sense enumeration and sense derivation schools in computational lexicography may be shown to be of less importance than suggested by recent literature
this form is like that shown in figure NUM
building knowledge bases for the generation of software documentation
figure NUM the saving procedure graph
this is done with a graphical outlining mechanism
supt ort multiple styles and multiple languages
words in brackets must be further specified
this tool produces a draft of the instructions in english and french
let us then see how this solution works for the problematic examples in NUM
that solution tbllows now naturally and iinmediately from the assumption that the elements of each arg s value receive the non linear order of NUM b principle z alone is thus now enough to make the correct predictions about ziji as soon as the o command relations arc established over the binding obliqueness hierarchy of multiclausal sentences displayed in NUM lcb typical of languages with subject oriented reflexives
giving some attention to the first of these two assumptions it is worth noting that not only the binding principles remained unchanged but also the formal notions used in its make up e.g. the relations of o command and o binding were kept unaltered
on the other hand by means of the linear order hierarchy assigned to these elements syntactic generalizations concerned with word order binding alternations etc are also regist ered i ollard and sag NUM ch NUM
a maria thlou acerca do l edroi corn ele i maria talked about pedro to him the ungrammatically of these examples shows that the pronoun is not locally o free there and consequently it is not the case that the local antecedent does not o command it
as to passives in russian the lexical rule among other things must give a new ordering to the arg s wdue where all ar i NUM i n are preceded only by argl and art2
in this connection the crucial point i want to m gue li r in this paper is that in order to increase the syntax semantics interface accuracy the reshutiling of the old subcat list must be hrther extended
data involving subject oriented rellexives both in active and passive constructions subject oriented reflexive pronouns and reflexives in double oblique constructions presented difficult apparently unrelated puzzles tbr the current binding theory which received a neat and unified solution under the present account
we can also distinguish local and head features as postulated in hpsg
another way to extend our grammar formalism is to associate weights with patterns
there is no dis null crete line between patterns and bilingual corpora
therefore we have been testing the integration method with the following steps
complete n gives the set of possible parses for the input i
these preferences can be expressed as numeric values cost for patterns
in this section we briefly review prioritized circmnscritltion
for exmnplc t hc scntel c
a NUM logical representation of sentences and background knowledge
wc argue l hat NUM rioritize d
in this paper we explore this direction further
the text was analyzed with swatwol and the results in regard to ambiguity are given in table NUM
the dispatching weights are dynamically adapted during the parsing see section NUM their initial values issue front the compilation ol the lexical subcategorization flames
besides the independence of microsemantics from the grammatical shape of the utterances warrants its robustness remains relatively unaltered standard deviation cyn NUM NUM
i introduction the need of a robust parsing of spontaneous speech is a more and more essential as spoken human machine communication meets a really impressive development
because of its dynamic and uncontrolled nature spontaneous speech presents indeed a high rate of ungrammatical constructions hesitations repetitious a s o
the semantic priming is a predictive process where some already uttered words priming words are calling some other ones primed words through various meaning associations
this unification must respect four principles unicity any argume b nust be at the most fulfilled by a unique lexeme or a coordination
primable words rejected words the primed words aims at constraining the speech recognition thereby warranting the semantic coherence of the analysis
the parser masters indeed most of the spontaneous ungrammatical constructions without any specific mechanism repetitions and self corrections repetitions and self corrections seem to violate the principle of unicity
as noticed previously with the coordinations these principles govern preventively the contextual adaptation of the network weights so that any incoherent priming is excluded
frame patterns as that of learning a lnulti dimensional discrete joint distribution where raw doni variables represent case slots
in particular we propose to employ an efficient learning algorithm based on the mdl principle to realize this task
can be written approximated as follows it al proximately satisfies such an assumption
ve employ suzuki s algorithm NUM o learn case fralim patterns is dendroid distributions
we conducted sollle experinlelits to automatically acquire case fi alne patterns from the penn free bank bra cketed
we address the problem of automatically acquiring case frame patterns selectional patterns from large corpus data
this problem has been inw stiga ted in the area of machine learning and related fields
a dendroid distribution can be represenled by a dependency forest i.e. a set of dependency trees whose nodes represent the random variames and whose directed arcs represent the dependencies that exist between these random w riahles each labeled with a number of parameters specil rcb ing the probabilistic dependency
o l w agg d non l rackt t cll
NUM l c an dip to NUM
the rhetorical parsing algorithm is outlined in figure NUM
table NUM evaluation of the marker identification procedure
overall we randomly selected more than NUM texts
we address now each of these objections in turn
they can cause the setting of a flag
the trees are ordered according to their weights
let b kln p denote the probability that k links are observed out of n co occurrences where k has a binomial distribution with parameters n and p
to what extent word order can be altered and how easily known information can be elided are also largely dependent on the syntax of each language
event pre state fill state value not full container a activity causer b post state fill state value d full contaiiier a c0 tent c
a lexical item consists of the position of the word in the sentence the root form of the word and its feature values
the format of ne and co outputs do not meet the standard of the scoring software because of arbitrary insertion and deletion of white spaces
binding theory constraint s reflexive pronouns must refer to a c commanding noun phrase in the same clause or the sam e noun phrase
the last component matches a money unit such as dollar cent dong or franc
we do think though that an abstract state in the domain model which subsumes a range of the concrete states is preferrable to introducing a primitive on the linguistic level unless the primitive is relevant for other linguistic phenomena as well
we wish to thank the oxford text archive princeton university and new mexico state university fo r making valuable data resources publicly available
the discourse module is being developed on a set of NUM dialogues totalling NUM NUM utterances
negative face or independence on the other hand concerns one s desire to maintain privacy and independence and to avoid the imposition or dominance of others
figure NUM the input as a prolog term
for data which is generally viewed as a test bed for focus theory utterances with multiple focus operators and second occurrence expressions we show that contrary to rooth s and krifka s theories the hou treatment yields a transparent analysis while avoiding under and over generation
not only did we show that this analysis is methodologically and empirically sound we also showed that it finds a natural realisation in the equational framework of iiou each linguistic phenomena is characterised by some equation s and the equations may mutually constrain each other
von fintel points out that in certain cases of adverbial quantification a focus operator associates with an unmarked tbcus and dots not associate with a marked tbcus occurring in its scope as should be clear fl om this article this is unproblematic for our analysis
in rooth s approach the i sv definition is purely semantic whereas in our approach the fsv is indirectly defined by solving equations and the value thus obtained i.e. the value of gd is a term that is a syntactic object
applying the semantics of only given in section NUM the se r antics of 4a is then as give in 4d NUM NUM a only wad the letters that NUM u1 NUM NUM
elaborating repairs like the example below may signal the listener about the status of the speaker s internal processing or reduce the facethreatening effect of the utterance
based on the information extracted from these tours and additional database material a prototype system ilex NUM generates hypertext pages for a sample page see figure NUM
to overcome this difficulty we settle with approximating the target joint distribution by the product of low order component distributions based on corpus data
fl olll the tdelltl rcb lt l
the first module the derivation reference choice component drcc suggests which parts of a pca are to be verbalized
f is a subset of g since f is directly governed by subset f and q in our rule above coincide here
rules in the third category involve more complex changes of the textual structure in a way which is neither a grouping nor an embedding
they form part of a chain if the conclusion c1 is used as a premise in the second step namely c1 e r2
by abstracting over concrete linguistic resources text structure should supply the planner with basic vocabularies with which it chooses linguistic resources
specified in terms of upper model concepts our semantic aggregation rules are more abstract than similar rules reported in the literature
sem s ca this definition can be easily extended into a subsnmption relation of japanese case class frames
if we can quantitatively evaluate though do tot in this paper to what degree an utterance alludes to the speaker s expectation to what degree it includes pragmatic insincerity and to what degree it implies the speaker s emotional attitude we think the proposed condition for recognizing irony can also be quantitatively defined
moreover candy s husband of example i can t erceive candy s ttterances la le as ironic even under the situation where he does not know or is careless of candy s expectation of satisfy her hunger in other words where he is not aware of the viqlation
dachshund ovcr there can really phty dachshund ow r there can really play i igure NUM spans u ticipa ting in the orru l
according to model c speakers should not hesitate to add extra prepositionm phrases to a noun even if this lengthens some links that are ordinarily short or leads to tagging or attachment mzj iguities
it also enforces i hc special direc lion dil y requiremenl s of dependency gra nnnar NUM he l rohibitions on cycles mm nlultiple par nl s
the focus of this discussion is on the linking relation used to extend left corner parsers rather than on the prediction step of the earley algorithm as with shieber although the results carry over
probability that word j gets linked to word i should b le i ally scnsilivc it should depend on the tag word pairs at both i and j
if we ignore capitalization the german word form runden round can be assigned the category n a or v the corresponding inflected forms are too numerous to list here but include e.g.
finally we list the linking relation compiled from the example with left recursive categories cat np cat np
whether this is linguistically important which is improbable or merely a mathematical game is beside the point the forreal problem is there and we can not individually specify infinitely many links
null various cf algorithms make use of a binary relation between a goal category and the category of a constituent phrase or word which either has just been parsed or is to be parsed next
in contrast to the previous example agreement specifications have been compiled out of the relation but no additional convention whereby eat specifications define a context free skeleton is involved here
when spans a and b are combined and one more link is added it is easy to compute the resulting span s score score a score b degr covering
the facts of german inflection lead to massively disjunctive analyses in conventional systems of morphological analysis that simply take an isolated inflected word form and consider what it might be
we need to introduce some additional notation
we informally describe the technique presented below
all remaining pairs are called dead
figure NUM transition function of g
the following three data structures are used
the tree language recognized by m is the
this is formally stated in what follows
we now turn to computational complexity issues
this paper explores the impact of certain basic implementational choices on the performance of various pba models
given the improved alignment and candidateranking methods better performance than dedina and nusbaum might be expected
the same bizarre pronunciation does not occur with the prod model
a similarly motivated pruning was carried out for the mp model
we have also inlplemented a version which uses a single heuristic
their pronunciation lattice differs with nodes representing junctures between symbols and arcs representing letter phoneme mappings
figure NUM simplilied prolmnciation lattice lor the lcxical word anecdote which fifils to produce any pronunciation
the shorter of tile two strings is then shifted right by one letter and the process repeated
although pba programs have been presented in the literature they are they are few in number
definition NUM the following definitions are relative to a given scfg g
the old prediction procedure can now be modified in two steps
the relation to lr parsing will be discussed in section NUM NUM
appendix b NUM suggests a way to implement this incremental summation
context free grammars are widely used as models of natural language syntax
they carry a lot of information
labels are represented under lud preds
figure NUM topic and conditional relations
for a picture of the discourse component here proposed see figure i
among them are a recorder for speech signms a hmm based word recognizer modules for prosodic syntactic and semantic analysis dialogue processing semantic evaluation as well as components for both german and english synthesis
to realize a prediction mode in this interaction a different schema was used at each frame the parser computes a set of possible continuations for each word i.e. it restricts the language model to pairs of words in case of a bigram model which are syntacticallly plausible and could be integrated into a currently existing syntactic derivation
the ils updates its internal configuration map taking care that split channel definitions are taken into account it then answers to the requesting component the individual tag used for this channel and the process identity of the target component NUM if the target component has not yet registered within the application this fact is acknowledged to the source component
we could have adopted the notion of agents cooperating to a certain degree while carrying out a certain task cooperatively but this would have meant to mix up different conceptual levels of a system the communication facilities we are describing here establish the means by which pieces of sottware may communicate with each other
of them possibly being realized in a different programming language or even a different programruing lmradignl demands complex interfaces between these modules l urthermore only modularization makes it possible to develop applications in a truly distributed inanner without the need to eol y and install versions repeatedly over
htrrent y there are NUM existing eon l otmnts that contribute to roughly NUM mill disk space the executables libraries and data liles required at rllntime use up NUM mb
the apl roac l here is to develop a speech translation system obeying design principles that have their orighl in the goal of constructing a system retlecting some of the assumed properties of human speech proeessing namely working incrementally fi om left to right and exploring the ell ects of il teraetion between dildrent levels of speech recognition and understanding
architectural experiment la addition to the employment wil hin the verl mo i il l rototype we used l jl as con mm ation vice or some exl erjt mnts i l the ra ntewor c
these grammars can be parsed extremely quickly but the coverage loss is in practice unacceptably high even with very large training corpora
other criteria such as trigrams and finer grained tags are obviously worth investigating and could be applied straightforwardly within the framework described here
phrasal parsing then creates a number of new edges including one for flight d l three one two as a noun phrase
in contrast our pruning takes place only at certain points currently before parsing begins and between the phrasm and full parsing stages
our method has points of similarity with some very recent work in constraint grammar NUM and is an alternative to several other related schemes
we coded for both true and implicit negatives and for both personal and impersonal expressions of modality
functions actions that represent the functionality of an interface object such as a menu item
null the next step was to undertake a comparative analysis of the lexico grammatical features found in the three genres
this paper presents the results of an analysis we conducted to this end on a corpus of software instructions in french
actions to be achieved by the reader are almost exclusively remised by imperatives directly addressing the reader
we highlight here three cases modality in goal polarity in constraint and mood in substep
we found that the second analysis corroborated the results of the first consistent with the nature of sublanguages
the preposition 1o can be identified as a constituent boundary
therefore a lot of computation is required in conventional chart parsing
NUM NUM have a reservation for tomorrow
the translation times are measured using a spare10 workstation
if the processed string is a content word e.g.
a summary of our approach will conclude the paper
this experimental result shows that our new translation strategy maintains translation qumity
table NUM possible linguistic sublevels in variables
number of substructures retained will the
this makes it possible to give a novel dynalnie account of sloppy id mtity t henomena i have shown that this approach aeeotmts for an expanded paradigm of sloppy identity going beyond tile data addressed in alternative a counts
section three oneerns an expanded paradigm for sloppy identity it is shown that the t roposed approach uniformly accounts for a broad range of sloppy identity phenomena including some not previously examined in the literature
payeheck ua save u4 ua the coinplete discourse is rel resented as follows null smith t past2 st end his t ayche k
by following hyperlinks the information seeker i.e. the user can easily navigate to new areas of interest
in other systems the hypertext pages themselves are generated at runtime as well as the links between them
examples are drawn from a number of systems including our own prototype ilex0
ruta petri net based hypertext document structure with browsing semantics a cm transactions on information
it was work like this which directly inspired work like the roger morris brooch we looked at earlier
in a system which opportunistically both satisfies user curiosity and its own informational goals we can provide an ersatz author
it supports natural language input and output and generated text contains hypertext links into an existing static hypertext
in the experiment described here we are determining the extent to which those senses of an adjective that are associated with one antonym can be distinguished from those associated with a different antonym according to the nouns that the target adjective modifies
for example not a single color word is a statistically significant indicator for the sense of light although light blue light brown light gray and light green all clearly use light in its not dark sense
text utterance type e.g. story note book manuscript monolog phrase speech stanza though largely subsumable under concrete often have concrete realizations as for book note manuscript
NUM the total number of sentences containing the target word that were extracted to yield NUM such adjectival instances of the target was NUM for hard NUM for light NUM for old NUM for right and NUM for short
we do not offer arguments that our metagrammatical approach is the best description of human processing of cross serial dependencies just that it is another theoretical justification for the difference in processing nested dependencies and efficient processing of crossed dependencies
these classes of languages can be arranged into a hierarchy based on proper containment relations among them ps3 c ps2 c ps1 NUM c ps1 c ps0 ps0 is the least restrictive the most expressive
returning to the difficult case of light one of the NUM sentences in which an error was made when all nouns were treated as indicators see section NUM NUM is a spatula is also used for lifting light pieces of food
these like time period are largely subsumable under the concrete feature but there are also concrete instances of some of these nouns such as book that still select the not long sense of short
done unless the program can have access to either some lookup facility and or can iater ct with a human user
for financial texts the domain of our reference corpus the proper nouns are company or institution names and person names
when a pattern matches against the input the matched string is replaced with a general tag of the relevant type e.g.
tagit applies these patterns to each record within the input assigning the appropriate tag information in case a match is found
the tag usr is used if the text is tagged by a user defined tagger as is done when processing messy details
the actual pattern used in the implementation is more complex and goes up to NUM but the example shows the principle
auk reads input record by record matching user defined regular expressions and executing corresponding actions according to whether a match has been retold
the auk implemented tagger developed for this project tag t can be used as a stand alone tagger for sgml texts
finally runtinm is also improved and development eased by the fact that no grammar rules need be defined for parsing such sequences
i currency measure i range curmeasure
figure NUM shows the relationships between the mtmber of relationships a nd the ccuraey of positioning
mea ns the percenta ge of words tot which the most preh ra lde
thesauruses directly a l ct tilt calculated distances between words
is unknown words do not lne very ow frequency words
viewpoints are extracted by calculating th c typicmness of word to wo y t relationsh s
if the simib rity va lue exceeds a pre delined threshold the node is marked
we have incorporated into the analogical translation method a shallow syntactic analysis module that identifies clause and phrase boundaries and that converts some variations into lexical and syntactic features
verb action verbj y multimodal action
the re tuber of grammar tiles for muld tttoda l
expressions a re simple a nd slereotyped
ffa lnework for the mult i moda l
a s he lain referre d
this section describes the design process of a
description of i elete this circle using mm i cg
obj ct ol i pointing oh
about half of table NUM and all of table NUM are not listed in the dictionary
we could have chosen to include only the instances of the act rel class but the nouns in the other two classes seem similar enough to describe all of them with the same type
underspecified semantic tagging however assumes no finite lists of senses but instead tags each lexical item with a comprehensive knowledge representation from which a specific interpretation can be constructed
both the multiple reference and the sense enumeration problem show that lexical items mostly have an indefinite number of related but highly discourse dependent interpretations between which can not be distinguished by semantic tagging alone
all of the newly generated concept nodes are loaded into the system and the training corpus is run through the sentence analyzer again
tion a major knowledge engineering bottleneck for information extraction ie systems is the process of constructing a dictionary of appropriate extraction patterns
we compared these results with results produced na concept node may be activated by circus more often than it is proposed by autoslog ts
this algorithm applies circus to a preclassified training corpus and com tmost but not all noun phrases will yield a concept node
the examples in the righthand column show instantiated patterns for which autoslog generated concept nodes based on the general pattern on the left
autoslog created NUM concept node definitions but many of these concept nodes represented general expressions that will not reliably extract relevant information
as input autoslog requires a set of annotated texts in which the noun phrases that need to be extracted have been tagged
autoslog then determines which clause contains the targeted noun phrase and whether it is a subject direct object or prepositional phrase
in the direct object case the sentence the armed men killed john smith produces the pattern killed x
as we indicated in section NUM NUM a dictionary for text classification requires patterns that can discriminate between relevant and irrelevant texts
instead as in the human human setting clients were the primary accommodators
figure NUM rates of accommodation for coincidental overlap and all three settings
in the human interpreted setting we examine the accommodation between client and interpreter
those in the field of human computer interactions usually note simply that accommodation exists
thus it has important implications for the design of workable human computer interfaces
for this reason communicational efficiency will be a concern
item first was not accommodating and by extension
what does this tell us about the design of human computer interfaces
r j lcb i r v tk rcb v t k tile set of constraints on label j for variable i i.e. the constraints formed by any coinbination of pairs variable label that includes the pair vi t rcb
the figure clearly exhibits that even though corpus NUM is twice as large as corpus NUM the distribution of words per tags is very similar i.e. more than NUM of the words have only one tag and are thus unambiguous NUM of the words have two tags NUM of the words have three tags and about NUM of the words have from four to eight tags
in table NUM we demonstrate that the genotype decision for the nms vls v2s v3s genotype always favors the noun masculine singular form nms over the verb forms vls for verb lst personsingular v2s for verb 2nd person singular v3s for verb 3rd person singular out of the NUM words listed in table NUM NUM do not occur in the training corpus and NUM of them can be properly tagged using the genotype estimates
all possible taggings i.e. p limp p nmp r jmp and r nmp appear in the training corpus
some of the rules of his system and the fact that he uses a minimal training corpus suggests some similarities with our system but the main aim of the work is to investigate methods to combine supervised and unsupervised training in order to come up with a highly performing tagger
notice that a simple sentence coincides with its ee
at the conceptual level there are NUM ees
fin handhng various l roblems m nlp such using syntaciic information on all tlie 0theriwsr ls as the resolutien of stru tural ambiguities i n the whole text
thus an embedded sentence will contain several ees
the remaining NUM non prr pronouns have intersentential antecedents
for example in the embedded sentence NUM where either the reflexive himself or non reflexive pronouns him may be used it is more natural to make use of him
he told bill someone wanted to see him
the structure we studied is the embedded sentence structure
broadly speaking embedded sentences concern more than one fact
third hypothesis prr pronouns require special treatment
ees are represented as conceptual entities in our work
the acquisition of such a lexicon with or without the assistance of lrs involves a substantial investment of time and resources
if clause rel is deactivated the system incorrectly selects feeling as the antecedent because it is the most recent vp
the results are given in table NUM which also includes results on the complete corpus for ease of comparison
the vp elipsis resolution system vpe res operates on penn treebank parse trees to determine the antecedent for vpe occurrences
here the circled vp headed by take is the antecedent for the vpe despite the containment relation
here the antecedent for the vpe is care this would not be permitted by the filter
because of examples like this we believe head overlap or head match are preferable criteria for success
it is also likely that the vpe res success rate would be higher using a post hoc evaluation scheme
here the vpe occurrence would can not select as its antecedent the containing vp headed by said
parse tree for she was getting too old to take the pleasure from it that she used to
in the second experiment new spanishspecific trees are generated from the feature set optimized for english and applied to the spanish test text s e s
for english the training text consisted of NUM messages obtained from the english joint ventures e v domain muc NUM corpus of the us advanced research projects agency arpa
this work develops an approach to multilingual name recognition that uses machine learning and a portable framework to simplify the porting task by maximizing reuse and automation
while various name recognizers have been developed they suffer from being too limited some only recognize one name class and all are language specific
a multitree approach was chosen over learning a single tree for all name classes because it allows for the straightforward association of features within the tree with specific name classes and facilitates troubleshooting
table NUM performance comparison to other work
these templates built by hand use logical operators and or etc to combine features strongly associated with proper names including proper noun ampersand hyphen and comma
the development of natural language processing nlp systems that perform machine translation mt and information retrieval ir has highlighted the need for the automatic recognition of proper names
features are derived through automated and manual techniques
delimitation occurs through the application of phrasal templates
the completion step uses the original recursion without collapsing unit production loops
if the rule x already exists sum the probabilities
one approach to the problem is to build robustness into the grammar itself
in many applications ungrammatical input has to be dealt with in some way
these filtering steps are very fast as they involve only table lookup
we chose the symbol rl in this article to point to this difference
b pat thinks that chris will give the talk
discourse new topics are often subjects or situation setting adverbs e.g.
they both behave like discourse om entities
what did mary do this summer
however necessary adopting a linguistic approach is a difficult task as one of the main drawbacks of this approach is that it is time consuming as far as the building of the lexicon is concerned
agent vari human theme var2 book we can then use the reversed lexicon to generate sentences that conform to the given syn struc but substitute appropriate words or phrases in place of the variables
we first discuss the type of information which should be found in nlp lexicons whatever their use analysis generation speech robotics
large scale computational generation lexicons carrying semantic information are indeed not that common the obvious reason being that acquiring semantic information is a difficult and time consuming task
in this paper we address the issue of generating multilingua NUM computational semantic lexicons from analysis lexicons showing the necessity of relying on a conceptual lexicon
creating these reversed lexicons has produced a number of additional advantages beyond those originally envisioned mostly in the area of testing and evaluating the semantic analysis system
is the representation precise enough to correctly translate all meaning components or is a specific source term mapped into a generalised one from which the original meaning can not be recovered
reversing an analysis lexicon before addressing the issue of reversing the analysis lexicon we want first to show how we could acquire a large scale analysis lexicon
it is well known in computational lexical semantics that a sense enumeration approach only based on subcategorisation differences is computationally expensive and unrealistic from a theoretical viewpoint
this is estimated from the relative frequency of v in the whole training data namely NUM p v p c is the prior probability that a randomly selected noun belongs to c
in accordance with the dependence assumption the score of a path is defined as the minimum of the scores of its component edges
statistical similarity cajcula l i ms there are ma w similar words inclu tiltg isy words
the viewpoint of a no le node is defined s a list o od marker word
this task may be similar to word sense disambiguation which deter i hlles the correct sellse of a word from several predefined candidates
vig NUM marke d nodes i the thesaurus the woms plaue and ship re located tmlow vehi cb
we use restrictive relatiolmhil s with NUM he mark ers rather than wor l NUM grains for two reaso s
sa lieiit words is a llsl lcb f wilt lib with rio r lcb qati nshit rcb s
stlital rcb h t rcb osition iti tile lhesauru t l tl ikilowii word NUM
likewise when a rule is found in which c is the left corner the relation tests whether the mother co of c can be used to reach the goal c before an attempt is made to parse the sisters of c
a further goal of this paper is to motivate a shift of attention from linking as a mere filter to genuinely predictive linking as a valuable device for the top down transport of information essential for other aspects of natural language parsing
after analyzing john np the parser expects a vp NUM where vp n is used as an informal alias for a vp that subcategorizes for n complements
the difficulty of course is that vp NUM and vp NUM are schematic and that these rules recursively generate a denumerably infinite set of vp type categories all of which may give rise to distinct elements in the linking relation
on the basis of his belief it in easy to inr r front la to lb
it will be shown that this integration is possible and of benefit to both theories as well as to the construction of lexical entries
however the selectional restrictions for discourse referents do not differ from the restrictions given in the prototypical situation description cf figure NUM
furthermore for those predicates that take more than one argument it is the order of the arguments which additionally determines the selectional restrictibns
in general tile instantiation rules provide struc1generally this grammatical case assignment is suitable for about NUM verbs of the partial field to give
in the axiom defining change sign s prestate so s consequences sl and s2 correspond to init e21 and init e22
the bsf describing the field of change of possession with one object to be transihrred and the derived deep cases are given in figure NUM
exploiting the field specific possibilities to make some variables denote the same reference object by renaming of variables results in more specific bsfs
model the inl erface l e ween syntactic and semanti al glltrlents as a list of t airs
calvin lends hob es a tie lb calvin glaubt daft lobbes ihm die krawatte zuriick qeben
it is interesting to compare the performance of the different methods on these words
we now present in detail several of the results obtained with the word drug
in section NUM we report the results of tests we have conducted on the treebank NUM corpus
experiments show that this method performs well and can learn even from very sparse training data
they are defined by two independent features fllsior and use of r odalily
NUM for example the english based approximate calculation is done as follows
means the sum of frequencies of all elements
we have developed an extraction method that is free fi om the above drawbacks
third tile linguistic methods are restricted to extracting correspondences between compound words
the content words are divided into simple words and compound words
co occurrence data extraction l NUM NUM NUM
we executed the word correspondence extraction program for each document
nq stands for at string of one or more ns
i would also like to thank david magerman for his help with testing spatter
this introduces an additional term p uib s into NUM
in this case less context means looking at the pos tags rather than the specific words
parse tree the correct one c a dependency representation of b
the estimation method is analogous to that described in the sparse data section of this paper
the dependency model is limited to relationships between words in reduced sentences such as example NUM
and NUM announced so af NUM NUM np s vp
thus the parser searches through the space of all trees with non terminal triples seen in training data
NUM ilenee l a m is calculated mi l principle in natural language processing
we have proposed a tnethod of hierarchical hisfeting of words hased on laxge corpus data
NUM do lcb NUM NUM randomly select one noun rcmow it from t h subset it
in particular we compared the performance of an mdl based sinm ated anuealilag mgorithm in hierarchical word clustering against
NUM accuracy refers to the success rate given that the disambiguation method makes a decision
algorithm using simulated annealing with an energy function based on the blinimum description length mdl principle
translation patterns can be enhanced with unification and feature structures to give patterns additional power for describing gender number agreement and so on
the reader should note critical differences between lexicalized grammar rules in the sense of ltag and tig and translation patterns when they are used for mt
figure NUM an extract of reference model for type angioplasty
tile inimt triple is angioplastie f de f segment ii f
in our system the target conceptual representation is defined by a domain model expressed with cgs
this analyser has been implemented on top of a conceptual graph processing package embedded in common lisp
in our method both predicate and argument can make a step towards finding their semantic link
building such a domain model is generally feasible in sufficiently limited domains typically technical domains
we consider that each predicate pi is associated with the head concept ci of a model mi
the model assigns probability to every joint sequence of words binary parse structure with headword annotation
they are illustrated by figures NUM NUM
table NUM shows the pp results along with
for example speechin a speech recognition module might generate events such as speechstart or speechstop and also produce a message containing a recognized utterance
the message output from dialogue processing contains a predicate form of some content to be communicated and a set of modes in which this can be presented
we discussed the duke programming tutor a system that demonstrates the integration of many of these ideas which has been used by a number of students
the ordinal is multiplied by v the value of the ordinal on the intuition that the user may actually count out the number specified by the ordinal
it then modifies the mes null sage from dialogue to allow only user s current preb ered modes and passes it on to output generation
note that the api would not only make development faster and easier it would also allow multiple learning algorithms to be tested in a particular domain
releasing control to the other participant involves matcbing incoming utterances to expected interactions for the various available subgoals and following the user to subgoals where matches are found
specifically it attempts to discover key missing axioms in the proof that prevent its completion and that may be attainable with the help of the user
the operations of these two modules are intertwined
in the reba case few request form appear in the matrix clauses
the matrix clauses of reba are hardly any user s volitional action
each number shows the frequency of use in the examples we examined
unfortunately so far we have not had such a commonsense knowledge base
therefore the subject of the matrix clause should be a machine
in jal anese zero pronouns frequently make a sentence ambiguous
then can the subject of the matrix chmse be a machine
the exceptions are the same type of sentences as NUM
again we gloss over this technicality in our terminology
in figure NUM b the underspecified feature structure might be the result of a database request completing the description shown in NUM a
a state produced by scanning is called a scanned state
the syntactic structure in the past filters out irrelevant words and points to the important ones thus enabling the use of long distance information when predicting the next word
behind this is to well ha ndle domain specific expressions
the standard computational technique for viterbi parses is applicable here
their search strategy is an itemtive combina tion of two elements
in other hmgua ges such as japanese pa rser lmscd prmfi g
compared to previous research this data structure has the following advantages
we have described a new method for learning bilingual collocations from parallel corpora
not therefore be part of a higher probability parse
parses are ranked by the following quantityg
this unification process is driven by the candidate verbs their psemspec consists of an upper model process and the mappings from situation elements to process participants which is achieved by co indexing with positions in the denotation
actor b acres a destination c when matching it against a sitspec with a tank and water this yields the verbalization the water filled the tank covering only the post state of the sit spec
a central assumption of the research reported here is that the deepest level of representation is in general not a linguistic representation instead we assume a domain model of some sort implemented in a ki i
lemma NUM a for all paths NUM starting with a nonterminal x p NUM gives the probability of the partial derivation represented by v
waiting for convergence is not a good policy and so alternative stopping criterions must be studied
where i j for label j in wtriable vi and sij the support received by the same combination
fbr each variable compute the support that each label receives froln the current weights
the experiments reported and the conclusions stated in this paper seem to provide a solid background for further work
probabilities may be good for n gram models but it is dubious whether it can be generalized to higher degree constraints
the back off technique as well as the trigram model requires a really big training corpus
where q NUM p presented with the input a the only string the grammar generates after one cycle of prediction the earley chart contains the following states
each variable ex is initialized to p x e and then repeatedly updated by substituting in the equation right hand sides until the desired level of accuracy is attained
to generate an instruction for performing a task is to chose some task elements to be expressed and linearise them so that they form a coherent set for a given goal the user might have
integration of document detection and information extraction
we also noticed that phrase disambiguation step was critical for improved precision
typical results are summarized in the table below words annotations both merge
we allow strict match terms to be included in the search queries in a specially designated field
precision at NUM top retrieved documents jumped from NUM to NUM
the compatibility of intentions is determined using simple heuristics
this is the accepted way of presenting temporal data
i t97 NUM i973 NUM NUM NUM annee
it s based on two information sources first scores that originates from the network output activations second a formal feature structure specification stating what mixture of feature pairs are consistent
the complete parse depends on many neural networks
this causes local dependencies to be learned first
the third network groups phrases together into clauses
the next network groups words together to phrases
four millions of training sentences are required
the handmodeling effort for feaspar is NUM weeks
frame or topic and a feature value
the handmodeling effort tbr the lr parser was NUM months
for pure tagging lemmatising purposes a reduced tagset not suited for sentence analysis can be used as well
task structure is constituted by five types of task elements which we define below
total rwordseg rliftana refine sol i NUM
this handles cases such as geb e
such a schema is e.g. the following head compschema
they are descriptive texts from the domain of economy
the results are given in table NUM
NUM NUM evaluation on a large data set
this is in contrast to established practice in the machine learning community
the processing speed of lexas is satisfactory
this approach integrates a diverse set of knowledge sources to disambiguate word sense
we estimate that there are NUM NUM errors in our sense tagged data set
the input to a wsd program consists of unrestricted real world english sentences
this assignment method does not even need to look at the training sentences
null we evaluated lexas on this larger set of noisy sense tagged data
figure NUM l exical rules as horn clause constraints
but verstehen is a main verb not an auxiliary
in this paper we address neither of these two issues
NUM k6nnen wird peter des auto gekauft haben
according to kiss specifying two different case wtlues under one reentrancy cf
while parse actions might be complex for the action interpreter they are atomic with respect to the decision structure learner e.g.
the frames to be combined are typically but not necessarily next to each other at the top of the stack
note that the first two steps together determine the tag for a word and the third determines the topology of the tree
each rule in the grammar is associated with a primary and secondary head and head information is passed up the parse tree
with a growing number of parse action examples available the system as described below in more detail can be trained using those previous examples
the first NUM sentences used in this experiment vary in length from NUM to NUM words averaging at NUM NUM words and NUM NUM parse actions per sentence
the traditional approach of trying to master the complexity of parse grammars with hand coded rules turned out to be much more difficult than expected if not impossible
r NUM to vp as pred 01aj pat and the system executing it repeating this sequence until the sentence is fully parsed
in such a partially trained system the parse actions are then proposed by the system using a parse decision structure which classifies the current context
particularly during the early development of our system this set was increased whenever parse examples had identical values for all current features but nevertheless demanded different parse actions
trained on the same NUM NUM sentences as spatter it relies on a much more limited type of context than our system and needs little background knowledge
features can in principal refer to any one or several elements on the parse stack or input list and any of their subelements at any depth
a knowledge base kb which currently consists of a directed acyclic graph of NUM mostly semantic and syntactic concepts connected by NUM is a links e.g.
applying machine learning techniques the system uses parse action examples acquired under supervision to generate a deterministic shift reduce type parser in the form of a decision structure
many of the mistakes are due to encountering con type of deci plain hier
to comment on something l sich zu etwas dat i iut3ern
questioning the traditional n grams magerman already advocates a heavier reliance on contextual information
before the parsing itself starts the input string is segmented into a list of words incl
parse features can be thought of as functions that map from partially parsed sentences to a value
the sequence of correct parse actions for a sentence is then recorded in a log file
parse actions can have numerous arguments making the parse action language very powerful
synt verb figure NUM example of a parse action simplified
when a word or a phrasal word is found in the lexicon a lexical item is created for each of its usages
if the weight is negative np1 and np2 ar e incompatible diff np1 np2 is asserted
a lexical item will be created that spans on the sequence of words and contains the semantic featur e value money
the pie system is implemented in about 38k lines of c about 33k to 34k lines were written befor e muc NUM
the positive weight is then adjusted according to the proximity of np np2 the grammatical role of np2
a non reflexive pronoun can not refer to a c commanding noun phrase in the same clause or th e same noun phrase
for example the static rule creates the records in NUM if any of the expressions i n NUM is encountered
if the post or organization is not found in the dependency tree then the person s positio n in the post holder database is used
for insl ance a el rcb tan e thresholds of evide nce weights initially set higll can be gradually decreased to allow more recall while keeping l rcb recision at a reasonable level
place names such as cities are nonmflly capitalized sometimes are followed by a state abbreviation as in albauy ny and may be preceded by locative prepositions e.g. in at from to
no lexi lcb on veriti lcb ation see later has been used in order l o show m re clearly the behavior the learning nmthod itself the l rcb erformance can be enhanced by lexicon verification
products may have no distinctive lexical appearance but they tend to be associated with verbs such as produce manufacture make sell etc which in turn may involve a company name
this evidence discovery an be relmated in m bool strmpl ing process l y ret la ing the initiml set of seeds with the new set of entities obtained froln the lmst itermtion
first in the semantic cat cgorizal ion l ro lem t here is al lemsl one olmn ended catc gory serving as m grml NUM rag for roll things non relevant
other concepl s such as equipment or materials have r w if any ot vious associati ms with the surrounding text and on may prefer just to iioint them out directly to the learning prograin
this in turn allows f lcb rcb r t rcb ootstrat rcb t ing pr rcb cess to gather more contextual evideal lcb c more quickly and thus to onwuge faster t rodu lcb ing better results
these are probabilities that k u v links were generated by an algorithm that generates correct links and by an algorithm that generates incorrect links respectively out ofn u v co occurrences
we show how the encoding of lexical rule interaction can be improved by specializing it for different word classes and in section NUM focus on an improvement of this specialization step by means of program transformation techniques
in the fourth compilation step section NUM NUM these automata are translated into definite relations and the lexical entries are adapted to call the definite relation corresponding to the automaton fine tuned for the natural class to which they belong
the intuitive idea behind this improvement of the covariation encoding is to lift into the extended lexical entry the information that is ensured after all sequences of possible lexical rule applications for a particular base lexical entry have occurred
this corresponds to actually unifying the out specification of a lexical rule with the in specification of the following lexical rule along each path in the automaton instead of merely testing for unifiability which we did to obtain the follow relation
one could therefore improve the calculation of frame predicates by taking the base lexical entries into account at this stage of the computational linguistics volume NUM number NUM i v v n figure NUM finite state automaton representing free application
the latter approach is in fact independent on how the criteria influence each other
the more examples the system observes the more reliable will be its decisions
in addition proper names resemble definite noun phrases in that their intended referent may be ambiguous
weak entity types however are allowed to merge with stronger entity types
the algorithm is specified in figure NUM example NUM continued we describe a run of algorithm NUM working with the sample tts g e r previously specified see figure NUM
note that mutual information is reliable in this case because the frequency of each word chunk is thresholded at the word chunk extraction stage
although this idea is corre t the itera ire combination strategy generates a mlmber ol useless expressions
german marks at NUM NUM p m extracted because japan and the u s is used so often independently as in japan and the u s
however japan and the is not extracted because it always appears in the context of japan and the u s
automatically acquiring conceptual patterns without an annotated corpus
figure NUM an instantiated concept node
consider the contingency matrix shown table NUM for japanese word chunk cjp and english word chunk c g
as depicted in figure NUM this remarkably reduces memory use because the pointer table also contains other string characteristics as figure NUM
this problem is particularly acute for translation since the decision as to whether to regard a sequence as a single unit depends on whether its components can be translated compositionally
with regard to expressiveness we believe that almost all variation in the order of arguments in a syntactic frame can be accommodated a syntactic frames generally contain four or fewer subconstituents
btgs make a favorable trade off between efficiency and expressiveness constraints are strong enough to allow algorithms to operate efficiently but without so much loss of expressiveness as to hinder useful translation
matters are complicated by the presence of the bigram model in the objective function which word alignment models as opposed to translation models do not need to deal with
translation accuracy was performed on a random sample drawn from chinese sentences of fewer than NUM words from the parallel corpus the results of which are shown in figure NUM
the number of possible alignments compared against the unrestricted case where any english word may align to any chinese position drops off dramatically for strings longer than four words
a noun is composed of a class prefix and root
rule s NUM removes an object reading of the word form
sets is a section where groups of tags are defined
in constructing the lexicon underspecification of analysis was avoided
fifth various semantic functions of word forms are also a source of ambiguity
in addition verb derivation also adds to the complexity of verbal morphology
after that a selection rule s NUM is applied
table NUM ambiguity after processing with the swahili cgp
this can be done by grouping the rules into separate sections
select i NUM pl n i ncl NUM gen con
if recognition is successful the gesture agent would assign the gesture an interpretation like that in figure NUM
of ambiguities will increase drastically when the length of an input sentence where NUM and i2 denote any two interpretations
i also thank greatly dr n abe of nec and dr y den of atr for their valuable comments and suggestions
since category type can be specified within a cfg rule syntactic preference can be defined as a function of a cfg rule
to prefer an interpretation with a higher lexical likelihood value then is to prefer it based on its lexical preference
NUM and r NUM are thresholds in the experiment described later both are set to NUM
note that in lines NUM and NUM iptex i1 pzex i2 l r i holds
the result indicates that there are more attachments attached to nearby phrases than are attached to distant ones in the training data
resnik has defined a probabilistic measure called selectional association in terms of the word classes existing in a given thesaurus
NUM after analyzing the sentence in NUM for example we obtain the case frames of the interpretations
if c NUM the input sentence is onsidered correct and if c NUM and improvements have not been found it is considered quasicorrect
figure NUM results using alignment information on en
figure NUM final result of merging process on transducer
results of our alignment algorithm are summarized in ss6
use he in forma l ion in a r stricl l it main or oo fine grainc l grmmna ti al
this work was partially funded by icsi
figure NUM results on three rules composed
our current algorithm and most previous algorithms are designed for obligatory rules
one can imagine processing sx first and then sy as if they are a linear sequence of simple sentences and applying the centering theory to each sentence successively and updating the data structures for centering
null in case of conjunctive postpositions of class b the antecedent tends to lie not the subject of the other sentence if one of tile sentences has the zero pronoun of the subject position
in addition to the above rule kameyama s version uses the property sharing constraint that two zero pronouns in adjacent sentences which co specify the same cb should share one of the grammatical properties
a NUM the range of search for the antecedent since the centering theory uses only the infornmtion in the previous and current sentences this might be problematic when we adopt the partition approach
and if a sentence contains multiple verbs we partition it into multiple simple sentences and apply the centering theory to a sequence of p rtitioned simple sentences individually for the zero pronoun resolution
null by structurally matching the japanese english parallel sentences in example NUM the following bilingual surface case structure is obtained
only surface forms of english verbs and case labels are used and sense distribution of english verbs is not used
in general c1 e2 means that cl is subordinate to c2
in roget s thesaurus sense classification is preferred to part of speech distinction
clusters of intransitive senses are discovered with the japanese case class frames which contain the qa
besides as the first term for measnring the generality of the association we use
in both thesauri leaf classes sthe classes of bgh are represented as numercorrespond to one word
as a result the human instructor is frequently asked to judge the correctness of the clue
let NUM and a be the sets of all verbs and norms respectively
a thesaurus is regarded as a tree in which each node represents a class
the words chosen have to match the user s expertise
in the first case he could build the tree from top to bottom
step e the instanciation of the variable place might yield river
computational feasibility is ensured by the fact that the above functional definitions are procedural in nature and can be converted to segmentation algorithms as well as by the implementable heuristic guidelines which deal with specific linguistic categories
this entry has underspeeified semantics with respect to the semantic constraints on its second and third argmnents as suggested in NUM
to illustrate our approach we will propose an account of a subset of verb alternation phenomena which rely on what are essentially underspecilled lexicm entries
available iraplementations of horizontal relations fail to satisfy the reasons that dictate their implementation the on need generation of lexical entries and efficient parsing
focusing is often associated with new information but it is well known that old information for example pronouns can be focused as well
in a similar fashion the three possible trigram layouts left middle and right are shown in fines NUM NUM
NUM steps for building an optimal training corpus this section explains the motivations of our claims for developing taggers for a language
the cost of the transition for tagging t is the negative logarithm of ft divided by f log ft f
the empirically determined values of the biased cost are as follows trigram biased cost bigram biased cost unigram biased cost
in the context of a small training corpus the problem of sparse data is more serious than with a larger tagged corpus
in this approach they use the notion of word equivalence or ambiguity classes to describe words belonging to the same part of speech categories
NUM the crossing error bracket pairs and spurious bracket pairs were evaluated by hand
ttb total treebank brackets number of brackets in the treebank
the generation rate shows that both systems arc rather modest in producing brackets
table NUM gives the measures and table NUM gives the results in percentages
until now a number of problems with evaluation have been pointed out
NUM next all resulting empty bracket pairs are removed
this leaves only the crossing errors and spurious brackets to be evaluated
the generation of an instruction based on a specific verb involves the rules rx and r2 see section NUM NUM these rules make correspondences between the conceptual predicate of the action and a specific lexical item
in accordance with the stratified framework of mtt the target representation of the lexicalisation process of close is a deep syntactic representation mainly a dependency tree whose nodes are labeled with full lexemes and lexical fimctions
thus in our system we plan to concentrate on correcting errors that involve features that are at or slightly above the learner s placement in slalom
however chaudron points out that there are a couple of simplification strategies that may not always be beneficial cha83
as an example of the way slalom can be used in correction consider the apparent similarity in the following two mistakes taken from different sources
iluinber of occurrences of a string in a longer string is illustrated with the de nominator of the fraction in equation NUM the bigger the nulnber of strings a substring appears in the smaller the fraction num o occu the bigger the c value of the string
NUM for tile NUM grams the the wall street journal occurs in two longer n grams and therefore gets its c value from equation NUM froin this string t wall street NUM NUM NUM and c wall street NUM NUM NUM
table NUM shows all the strings that appear more that twice and that contain wall street
they extract a substring of a collocation if it appears a significant amount of times by itself
these corlms based at preaches have also been used for the extract ion of collocal ions
dictionary consl ru i ion and secon NUM language learning t o mmm a few
therefbre t wall street NUM and c gall street l
the measure is called c value and the fa tors involved are the string s frequency of o eurrence in the corpus its fre luen y of oe urrence in longer candidate collocations the immber of these longer andidate ollocations and its length
its c value is ah ulated l rom equation NUM for each substrings eon ained in the NUM gram tile number NUM NUM the l requen y of the NUM gram is kept as its till now fl equeney of occurrence in longer strings
consider the following magic rule magic vp vform cgemlargs ssem magic vp vform args ssem
there are two possible solutions for cat NUM as a result of the fact that the filtering resulting from the magic literal in rule NUM is too unspecific
the modified versions of the rules defining nps are adapted such that they percolate up the index of the guarding magic fact that licensed its application
as a result of the explicit representation of filtering we do not need to postpone abstraction until run time but can trim the magic predicates off line
our results for disambiguating turkish indicate that using about NUM constraint rules and some additional simple statistics we can attain a recall of NUM NUM and a precision of NUM NUM with about NUM NUM parses per token
in languages like english there are a very small number of possible word forms that can be generated from a given root word and a small number of part ofspeech tags associated with a given lexical form
a rule r c1 NUM cn v will match a sequence of tokens wi wi l wi n NUM within a sentence wl through ws if some morphological parse of every token wj i j i n NUM is subsumed by the corresponding constraint cj i l
after the application of constraints as described above for 6the reason for the comparatively high number of unknown words in man is that tokens found in such texts like NUM
in each case they found the m gument flawed the phenomena in question did not yield languages whose stringsets were homomorphic to tile duplication language
this is sufficient to yield languages thai more closely resemhle the ziirich dialect in having other constructions besides the duplication construction yet remaining efficiently processable
essentially t his obtains the reversm behavior nee ded of a st ack to process copy languages as well as rew rsals
however the situation is more involved than tile basic approach since there needs to be a way to indicate where the metagrammatteal approach is to be invoked
essentially we want to be able to write arbitrary ps3 or ps2 grammars and also be able to parse the string duplication language for whichever psi we choose
we suggest a parsing method for languages that rely on ww which does not cost a greater complexity fec than the worst case for parsing context fi ee grammars
not hard to process it is always possible to compile less restrictive grammar formalisms into more restrictive covering formalisms allowing different constituent analyses and potential stringset overgeneration
the elipses indicate that the remainder of the stack is passed on from the lhs to each nonterminal and only the nonterminals on the rhs
in english as a whole transitivity is indicated by a cluster of features associated with a clause
for the information retrieval experiment ten queries are put to a newspaper database a demonstration system running on wais wide area information server carrying two weeks of articles from the times newspaper from NUM and NUM
this paper describes an on going study which applies the concept of transitivity to news discourse for text processing tasks
for analysis the most likely rules can be applied first in the case of known senses and since nonce senses are by definition rarer rules will be applied productively only when this fails
we can assume for example that a verbal use of car will not be postulated by a generator because it is unattested and will only be possible for an analyzer when forced by context
recently techniques have been described which address the efficiency issues that this raises for fully productive rules such as inflectional rules and syntactic rules such as the hpsg complement extraction lexical rule
an action which is realis occurring in the real world is more effective than an action which is irrealis occurring in a non real contingency world e.g. they attacked the enemy they might attack the enemy
one fsm will represent the application of both rules of conversion zero affixation and rules of derivation to a given lexeme and the latter will change the form of the word and thus participate in a different distribution
basic entries are augmented with a representation of the attested lexical rules which have applied to them and any such derived chains where both the basic entry and these abbreviated derived entries are associated with a probability
if the derived form is irregular in some way then the exceptional information can be stipulated at the relevant state and the feature structure calculated by default unifying the specified information with the productive output of the lexical rule
a possible extension to the work here described would be to generalize the two layer model to other
a semantic grammar the tokens of whici are concepts thac the segment in question represents
one such situation is the presence of clarification and correction subdialogues at any point in the conversation
NUM NUM reithinger eta t9 NUM iida and yamaoka
NUM NUM so we ll switch you to a double room
the correct assignment of speech acts will provide a more accurate translation into other languages
an evaluation will be performed on NUM dialogues previously unseen by the discourse module
itowever the corrector can inform the user that the input sentence is syntactically ill formed and indicate the gaps in the synts i.e. the boundaries of the fragments which providc the minimal covering of the sentence for r NUM
in our experiments for initial sentences with syntss the mean processing time was NUM NUM seconds NUM NUM seconds per word the mean length of such sentences being NUM NUM words and the mean time of parsing was NUM NUM seconds
applying this system to problems with high noise such as reading handwritten texts or speech recognition seems more questionable observations show that when the density of errors increases the quality of correction becomes rather low
it shonld be noted that if we put r NUM then parsing on the extended morphs is actually reduced to that on the initial morphs which justifies the notation c NUM introduced in the previous section
in the course of parsing only those fragments are considered for which d r one can imagine that the parsing algorithm remains unchanged but creation of fragments with d r is blocked in the grammar rules
the arcs of a synts are assigned certain weights which express relative strength or priority of the corresponding syntactic relations the weight of the synts is equal to the sum of the weights of its arcs
though this method is simple it proved to be quite effective and reliable in most cases tl e corrector generates a single hypothetical correction while the probability of losing the right correction is rather small
here and below a sentence is called well formed if it has one or more syntss which are correct with respect to the hypothetical complete grammar otherwise a sentence is called ill formed
relevant texth nt recogmzes the mdmcator predmcates by simple pattern matclung as contmmng an
phone NUM NUM NUM NUM NUM fax NUM NUM NUM NUM NUM ben ks k fh hannover de
table NUM are hsted since the cuonary entries are annotated with mterpretauons the agent can draw the attention of other agents to these proposmons by passing them parts of its pnvate knowledge
ized with macromed a d rector commumcauon between clos and macromecha director is medmted by apple events
unless two criteria correspond to rules of the same conflict set
these features are based on suitably restricted production system techniques and on a generic backtracking regime
in word net since each node it the thesaurus is a set of words that haw synonym relationships synset wtrious methods for similarity cah ulatlon using the synset classes have been proposed
this p q er describes a method for positio ing unknown words in an existing thesa rus by using wordto word rela tionships with relation case markers extracted from a large corpus
it is important that the return vmues of the nctions should be depend on the corpus and the locm context of words s q he proposed method can t e used to remize these functions
the data a n l human judgements used in onstructhlg thesauruses would be very useful in nlp systems unfi rtunately howeve h this information is not represente i in the thesauruses
a tgl rule is successfully applied if the action part has been executed without failure
ne ted by is a relatio ships as well as syn mym rela i ionshil s and theref ore large areas rel resenl strong similarities to unknow words
tg NUM has an internal language gil that corresponds to an extended predicate argument structure
the ego variable is shown using indices running over the elements of the respective conflict sets
null backtracking may turn out to be inefficient if it involves recomputation of previously generated substrings
the expression okay can be a prompt for an answer NUM an acceptance of a previous offer NUM or a backchanneling element i.e. an acknowledgement that the previous speaker s utterance has been un null derstood NUM
taking into account only the speech act of the previous segment might leave us with insufficient information to decide as is the case in some elliptical utterances which do not follow a strict adjacency pair sequence NUM talking about flight times rcb s1 can give
using the example can this do this we describe tow sophisticate synergistic iuputs should be processed more precisely
olh onellt s as if they are r hysicaj objects cha r cterizajje via
more principled approach than simply expecting an application t o handh the plethora of tiverse inljuts its all heir forths
z j denotes the initim subsequence of z up to and including the ju leaf label
this research was in part funded by a nato science for stability phase iii project grant tulanguage
figure NUM l rie representation of the NUM trees in fig ure NUM
l o a leaf of the trie is reached without viola tittg
less than vi l based on the total ordering of the vertex labels
we then ran ore algoridnn on these data sets and obtained perfof mance information
needed for the computaiaon ol7 ll i l j NUM
and q i is a terminal node then output v end end disca rdod
we a sso iate the f ollowing costs associated with these lifl erences if i oth trees have a
this means it is particularly prone to burstiness and unpredictability which affects all levels of n grams including unlgrams
the analysis of the NUM NUM bnc fries was therefore repeated using the loglikelihood instead of rank correlation as the similarity measure
for a lig l and an input string x of length n we build a non ambiguous context free grammar whose sentences are all and exclusively valid derivation sequences in l which lead to x
ligs are a restricted form of indexed grammars in which the dependence between stacks is such that at most one stack in the rhs of a production is related with the stack in its lhs
if we assume that an a production is generated iff it is an s o3 production or a occurs in an already generated production we get
we show that this grammar can be built in NUM n NUM time and that individual parses can be extracted in linear time with the size of the extracted parse tree
for which the relation p holds between a and b
this agrees with intuitions based on a manual inspection of the contents of the email corpus
similarity and homogenei w of bnc domains and email table NUM shows both sets of results
however many recognition applications are more constrained often to a specific topic or domain
evidently the strongest correlation with the email corpus is from as applied science
however both the rank correlation and loglikelihood ratio both make use only of unigrarn information
which is significantly lower than that produced by the rank correlation mean rank NUM NUM
in fact they seem to be necessary to obtain a linguistically correct description of coordination in german
for this reason we have tried to disambiguate all the nouns from real eneko agirre was supported by a grant from the basque goverment
bear in mind that models a b and c do not themselves specify probabilities for all spans intrinsically they give only probabilities for sentences
6different k g spans have scores conditioned on different hypotheses about tag g and tag g NUM their signatures are correspondingly different
suppose that the markov process when gem crating a child remembers just the tag of the child s most recently generated sister if any
the corpus was derived by semi automatic means from the penn treebank only sentences without conjunction were available mean length NUM max NUM
in the pilot unlike the full experiment the parser was instructed to back oil from all probabilities with denominators NUM
in practice solrre generalization or coarsenlug of the conditionm probabilities in NUM heaps to avoid tile e ll ets of undertrmning
a subtree s of t is also indicated using underlined labels at nodes of t note that s and s have the same number of leaves
the standard algorithm checks each rule in r for application to an initial parse tree t trying to match the left hand side of the current rule at each node of t using the notation of theorem NUM the running time is then o irplti
we also write lhs r for lcb lhs r i r e r rcb recall that we regard lhs r and lhs rj i j as different objects even if these trees are equivalent
we call active each pair n i such that lhs ri matches c at n at the time i is retrieved from h as already mentioned these pairs pass the test in the head of the for loop in the main program
also we now have rule l lcb m16 rcb rule NUM lcb m27 rcb recall that m17 m27 rule NUM lcb m27 rcb and h contains indices NUM and NUM
while that result uses a representation of the pattern set our set lhs r requiring an amount of space which is exponential in the degree of the pattern trees as an improvement our transition function does not depend on this parameter
the next call to update associated with the application of rl updates the associative list state in such a way that state m27 q9 lcb n35 rcb and no other final state is associated with a node of c2
given trees t and s s a subtree of t we write local t s to denote the set of all nodes of s and the first ha proper ancestors of the root of s in t when these nodes are defined
in this section we will discuss three solution to the protflems mentioned before
normally the grammarian knows which information needs to be made explicit
this structure specifies interactions between these levels by means of corderences indicating the sharing of information
a simplified exmnple is shown in tile lexical entry for the verb come in fig NUM
tile parser has to identify those paths in tile lattice which represent a grammatically acceptable utterance
we have indicated earlier that type expansion can be fruitfully employed to preserve the coref skeleton
though highly attractive theoretically using such codescriptions for analysis creates problems of efficiency
in the following section we start with a top down view of the architecture
the efficiency of the parallel running system mainly depends on that of the syn parser
the other component then works only with successflfl analysis results of the previous one
it prevents us dora proving the unsatisfiability of 14a b but we can still show the inconsistency of the clause representation of 13c and 14tl as in NUM
a fundamental issue for parsing lies in the area of unknown or new words
the total parse accuracy of dop4 reaches NUM
the output structures reflects the underlying head modifier relations e.g. die neuartige und vielf ltige gesellschaft yields sere head gesellschaft mods neuar tig vielfaeltig quantifier d det agr nom accval end
shallow parsing is basically directed through fragment combination patterns fcp of the form fsttelt anchor fstright where anchor is a lexical entry e.g. a verb like to meet or a name of a class of lexical entries e.g. transitive verb
NUM the overall architecture of smes the basic design criterion of the smes system is to pr r de a set of basic powerful robust and efficient natural language components and generic linguistic knowledge sources which can easily be customized for processing different tasks in a flexible manner
a major drawback of our current approach is that necessary and optional constraints are defined together in one fcp
the edge var var is used for simply skipping or consuming a token without any checks
daimler benz number date and time expressions are normalized and represented as attribute values structures
for example the following basic edge mona cat partikel pre tests whether tc produced by mona is a particle and if so binds the token to the variable pre more precisely each variable of a basic edge denotes a stack so that the current token is actually pushed onto the stack
we use variables of the form ui to denote ordinary extensional individuals we use variables of the form xi to denote dynamic individuals
we will extend the fragment in the following ways we will add the idea of a di scour se
the above t aginent following the kamp lteim accounts considers only one type of anaphora involving individuals
we now show the deriwttion tbr NUM example NUM can be derived in a similar fashion NUM
constraints on the resolution of anaphoric expressions arise in part from the ways in which the center can change in a discourse
below i will argue that tile dynamic account is more general and empirically adequate as well as being simpler than alternative accotmts
these examples show that while the conjunction finder und hilft can not take either a purely accusative 5a or dative complement 5b it can combine with the np frauen 5c which can appear in both accusative and dative contexts
the modifiers of a word w are the head words of the specifier complements and adjuncts of w
NUM we provide an lcg derivation of 2b in figure NUM roughly speaking rule p allows both the ap wealthy and the np a republican to weaken to npvap so the conjunction satisfies the antecedent of the predicate became
this paper has examined some of the differences between a standard complex feature structure account of agreement which is fundamentally organized around a notion of consistency and an account in an extended version of lcg in which agreement is fundamentally an asymmetric relationship
vp npvap npvap e wealthy a republican grew and vp ap and np vp ap conj vp ap il npvap p conj npvap p vp ap eo npvap eo as in the previous example the lcg approach does not require the case feature to be decomposed
in this paper i explain how we can use discourse knowledge in order to help a parser disambiguate among different possible parses for an input sentence with the final goal of improving the translation in an end to end speech translation system
since the module can be either incorporated into the system or turned off the evaluation will be on the system s performance with and without the discourse module independent graders assign a grade to the quality of the translation NUM
in this paper i have presented a model of dialogue structure in two layers which processes the sequence of subdialogues and speech acts in task oriented dialogues in order to select the most appropriate from the ambiguous parses returned by the phoenix parser
to summarize the two layered fsm models a conversation through transitions of speech acts that are included in subdialogues
the domain of the dialogues named travel plannin NUM domain consists of dialogues where a customer makes travel arrangements with a travel agent or a hotel clerk to book hotel rooms flights or other forms of transportation
in tagging we ask if the same word has already occurred in the sentence and ff so what its value is for various features
our approach aims at obtaining information both from the subdialogue structure and the speech act sequence by modelling the global structure of tile dialogue with a fsm with opening and closing as initial and final states and other possible subdialoguesin the intervening states
it should also be mentioned that such incorporations are not restricted to arguments
they provide the ability to introduce such attributes in an adjectival form
however this correspondence is not given as such in the lexicon
the figure i illustrates NUM 1e fill the hydraulic reservoir
the literal translation associated to sentence iv illustrates this point
carry out a careful cleaning of the filter body
the corpus shows that domain specific verbs are often prefered over ordinary verbs
by contrast this argument can be made explicit with the verb remove
NUM lexicalise the remaining arguments and link the resulting lexemic structures to v
on the other hand 3b is correctly predicted to be ill formed because the strongest possible category for the coordination is npvap but this does not imply the stronger ap antecedent of grew so the derivation in figure NUM can not proceed to form a vp
roughly speaking features will be treated as atomic propositions we have no need to separate them into attributes and values and a simple category will be a boolean combination of such atomic features since we have no reason to posit a recursive feature structures either
by introducing and withdrawing a hypothetical ap constituent as shown in figure NUM it is possible to conjoin grew and remained but the resulting conjunction belongs to the category vp ap and can not combine with the wealthy and a republican which belongs to the category npvap
fhc basic unit in the lexi on is a sense which is the inforlm tion denoting some indivisible predication along with the thematic roles involved
in this section wc present n hw exmnples that show how one c m describe a given verb sense
NUM constraints on verb features that describe any relevant constraints on tile morphological features of the verb such as agreement or voice markers
the semantics of such syntactic role fillers are usually determined by their lexicm semantic and morplmsyntactic properties instead of position in the sentence
vmency changing transfbrmations such as morptnologieally marked passivized or causativized forms are handled via le xical rules that manipulate case dames templates
in this paper we present an approach to building a constraint based case dame lexicon for use in natural language processing in turkish
l he agi t 13b3 agentive object enotes the equiwdent of the by objecl in passiw sentences
additional morphosynt ctic lexical and semantic selectional constrmnts are utilized to map a given syntactic argument structure to a specific verb sense
however such a recipe can handle only cases where the belief has been established by the action just before surface request
weight an important point to bear in mind is that a decision tree in general is a complete description in the sense that for any new data point there will be some leaf node that corresponds to it
for present purposes it is sufficient to observe that given this extended algorithm we can allow c in the expression c c p to represent a weighted regular expression
this paper reports on a marriage of these two strands of research in the form of an algorithm for compiling the information in decision trees into weighted finite state transducers NUM given this algorithm information inferred from data and represented in a tree can be used directly in a system that represents other information such as lexicons or grammars in the form of finite state machines
each of the symbols in the expressions for a and p actually represent sets of pairs of symbols thus alp for example represents all lexical alveolars paired with all of their possible surface realizations
when this tree is used in predicting the allophonic form of a particular instance of aa one starts at the root of the tree and asks questions about the environment in which the aa is found
the input c is predefined for the entire tree whereas the output c is defined as the union of the set of outputs along with their weights that are associated with the leaf node
what remains to be done is to integrate this construction procedure with the proposals we have made in this paper
an aspectual operator but rather as having wide scope over the eventuality as exemplified by the drs klb
another point worth noting is that the second proposal is also more complex from the ontological point of view
adding new types of discourse referents like in the language of drs requires that we define their ontological properties
to do this we are using a large corpus taken from french contemporary literature
we have looked at examples involving french passg simple ps simple past
among these examples a majority are of the form exemplified in NUM
the contrast in 5c 5d is clearly parallel to that in 5a 5b
we have a set of NUM examples of sentential negation in which one find only NUM occurrences of ps
we will therefore ass lelate with this node a partial i rs to introduce tim discourse retb rent
the client is inibnnation receiver and the interpreter is the imparter of information not the originator thus neither client nor interpreter is in a dominant role
in the human human setting client use of words in common made up a significantly greater percent of total word use than agent use of these words p NUM
figure NUM local nhyp vs global nhyp
all the links have the same weigth
table NUM data for each text
table NUM overall data for the best window size
we think that several factors make the comparison difficult
preceding graphs were relative to the polysemous nouns only
wordnct groups noun senses in NUM lexicographer s files
context size different behavionr for each text
formula NUM can be approximated in two ways
global nhyp is as good as local nhyp
their general strategy is to specify the interpretation of the antecedent clause as an equation between a propositional variable s and a predicateargument structure
the generalized reconstruction algorithm presented here does not require the presence of constituents in the antecedent corresponding to adjunct elements of the fragment sequence
these examples indicate that the possible values of case slots depend in general on those of the other case slots that is there exist dependencies between different case slots
ai lcb gi locat tm ai lcb g2 locat m ai lcb g2 location
we have shown that horizontal redundancy is inherent to a lexicon consisting of descriptions of fully formed objects
instead the single lexical entry is interpreted on the fly each time according to well specified constraints
most frequently horizontm relations are implemented as unary rules operating at parsing time within a dcrivationm component
the notion of lexical rule is often given some status at the level of linguistic or psychological theory
dative shift the locative alternation ctc and word formation phenomena inflectional and derivational morphology
one approach would involve defining two lexieal rules an alternative would be to express all three possibilities directly
viewed from an implcmentational perspective onthe fly application of lexicm rules brings with it a number of distinct advantages which follow from the drastic reduction in the size of the lexicm database lexical construction is less time consmning and parsing time should be reduced as lexical look up is less ambiguous etc
in the sections that follow irst we discuss the linguistic at l roaeh underlying our proposal second we eomlmre our proposal to existing underspecification tq proaches and finally we give some details of the implementation which relies on no special fc tures or external devices
l he contextual factor that resolves the ambiguity is the semantics of the head of the prepositional complement which here is tt ken to specii y whether the direct ob ieet of the verb is understood as the location and the oblique complement as the locatum or v ee versa
if this expression is combined with another expression the structurm ambiguity will be further compounded
in this method constituent boundary patterns are applied to an input in a bottom up fashion
NUM NUM x to y x goes NUM x a m
the following sentences cause much structural ambiguity because of pp attaehment relative clauses conjunctions etc
through preliminary exl erimentation our new ti mt has b e n
new method is much smaller than that of the number of possible structures in the top down method
this marker is inserted into the above sentence the bus noun verb goes to chinatown at ten a m
through preliminary experimentation our new tdmt has been shown to be more efficient while maintmning translation quality
in the top down and breadth tirst pattern application the above procedure is executed in the described order
the passive arc NUM is created by combining NUM and NUM
now we turn to the problem of how to select the best dendroid distribution fi om among all possible ones to approximate a target joint distribution based on input data generated by it
figure NUM variables used to estimate the model parameters
a link type is an ordered pair of word types
local maxima are like pebbles on a mountain invisible at low resolution
the probability function expressed by equations NUM and NUM has many local maxima
thanks to alexis nasr for hand evaluating the weather translation lexicon
figure NUM shows the precision of the model with NUM confidence intervals
missing links are more informative they indicate where the model has failed
the competitive linking algorithm and its one to one assumption are detailed in section NUM NUM
the model s precision recall trade off can be directly controlled via one threshold parameter
there are two extreme types of architecture which ma na ge the agents
for exampie sa ying delete the circle while pointing at
however the interpretation of such inputs should be referred to by other mode interpretations
a nd semantics of inputs of ea ch mode are defined with gramlna r
parallel lisa allows the user to employ multiple modalities sintulta neously
step NUM corpus collection the eorl us of multi i mda
for exampie pointing a t a n existing object while saying delete
n the largest intersection with the syntactic codes for attempt occurs with the verb try ti t3 t4 n
t fi asil ility of priorit ized ir uln script ion for specifying taw
this priority of the t references means that the formula isa a male a subj i a
thus there is a corrcst on lencc l ctwecn a solution of an hclp language and the most t rcferablc models of prioritized circunmcription
one method taken by the ntt data system in met NUM is to first tokenize with normal dictionary entries and then later to extract sublexical parts during ie
be lmedf0r gesoban i ron em
for exanlple a has b a also includes c
lthis translation was not NUM roduced by our syst m
listed are personal titles e.g. mr king organizational identifiers including strong identifiers such as inc and weaker domain identifiers such as arts and names of large places e.g. los angeles california but not scarsdale n y
for example professor of far eastern art john blake is parsed as professor of fax eastern art john blake whereas professor art klein is professor art klein
although the same relationship holds between the lexical head laboratories and the conjunction and in ibm and bell laboratories another heuristic takes precedence one whose condition requires splitting a string if it contains an acronym immediately to the left or to the right of the ambiguous operator
when encountering mrs candy hill in input text for example a machine translation system should not attempt to look up the translation of candy and hill but should translate mrs to the appropriate personal title in the target language and preserve the rest of the name intact
a name variant taken out of context may be one of many types e.g. ford by itself could be a person gerald ford an organization ford motors a make of car ford or a place ford michigan
in english capitalization usually disambiguates the two though not at sentence beginnings at the beginning of a sentence the components and capitalization patterns of new coke and new sears are identical only world knowledge informs us that new coke is a product and sears is a company
NUM cbs NUM cbs are the percentage of sentences with NUM or NUM crossing brackets respectively
this section describes the way p af j s b is estimated
the rule also has the benefit of improving efficiency by reducing the number of constituents in the chart
NUM elaborating this analogy the in feature of derived words can be understood as the dtrs feature of a phrase
in addition to conditioning on whether dependencies cross commas a single constraint concerning punctuation is introduced
the covariation approach described in this paper can be viewed as a domain specific refinement of such a treatment of lexical rules
this is accomplished by having each lexical rule predicate call a so called frame predicate which can have multiple defining clauses
all tests were made on a sun sparcserver 1000e using NUM of a 60mhz supersparc processor
the results are for all sentences of NUM words in section NUM using model NUM
further suppose the input segment ic is to be marked underparsed so that the final description s a e contains x a b i y d e
in example NUM the constraints fill m and fill p effectively ban overparsing cycles no matter where these constraints are ranked a description containing an overparsing cycle will be less harmonic due to additional fill violations than the same description with the cycle removed
a necessary property of the faithfulness constraints given constraint locality is that a partial description can not have overparsed structures repeatedly added to it until the resulting partial description falls into the same cell category as the original prior to overparsing and be more harmonic
null tesar identifies locality as a sufficient condition on the universal constraints for the success of his l in this paper tree structures will be denoted with parentheses a parent node x with child nodes y and z is denoted x y z
first consider once the overparsing operations for each non terminal x which has a production rule permitting it to dominate a terminal x each tries to set ix NUM to contain the corresponding partial description with the terminal x left unfilled
because each overparsing operation maps a partial description in one cell category to one for another cell category a partial description can not undergo more consecutive overparsing operations than there are cell categories without repeating at least one cell category thereby creating a cycle
if gen can be specified as matching input segments to structures generated by a context free position structure grammar and the constraints are local with respect to those structures then the algorithm presented here may be applied directly to compute optimal descriptions
the algorithm presented in section NUM fills the table cells level by level first all the cells covering only one input segment are filled then the cells covering two consecutive segments are filled and so forth
the final type of parsing operation fills a cell for a category which is a single non terminal on the left hand side of a production by combining two entries which jointly form the entire right hand side of the production
as noted abow each document within the trekbank is classified along many different axes in order to support a large variety of different task specific groupings of the documents
it is useful to iltl ensively sl udy fixed collocations because NUM he ollocatioll of lilore com plex structures is lillic lt to h i regardle of the mf l hod used
the complete bipartite connection between the contents and the messages means that either message can mean either content grammatically without too much cost
to have a more complete game theoretic account of natural language we need a quantitative characterization of how those factors contribute to the game
since fred was referred to by the subject and max by the object in ul fred is considered more salient than max in u2
first as mentioned in the previous section it is considered a repeated game which is a sequence of smaller games
this will be a critical move for the ot fst paradigm
they postulate two competing constraints align prefix and the higher ranked nocoda
figure NUM candidate outputs for um gradwet in an
created by a generator function gzn operating on input strings
the final candidate is pruned since it violates the align constraint more times than the winner
as in the ot tableau the winning candidate figure NUM violates nocoda twice
the regular language generated by this fst figure NUM has a very simple structure
a verb partition NUM v is defined analogously
it can be described by the regular expressions
we then build the union us i of all initial subsequences si and the union us n of all extended middle subsequences s e and formulate a preliminary sentence model
the fsts perform tagging faster than the hmms
NUM by means of the hmm transducer
different classes may contain the same tag e.g.
we are however interested in non weighted transducers
we believe that many of such characteristics carry specific communicative functions that must be preserved in order to obtain translations with high stylistic and pragmatic accuracy
verbal differences between french and english instructions can be classified along three interrelated dimensions NUM lezical french and english versions diverge because of differences in the lexical resources available in both languages NUM syntactic equivalent verbs exist but the two versions can not rely on similar syntactic constructions and NUM stylistic lexically and syntactically equivalent versions may be obtained but one of them would be stylistically incorrect
the position in the sentence uniquely identifies a word and thus its corresponding group of different morphological reading s
lr application can be chained so that the rule chains are expanded either statically in the specification or dynamically at application time
even with the best left hand side lhs conditions see below the lexicon acquirer may be flooded by new lexical entries to validate
research reported in this paper has exhibited a finer grain size of description of morphemic semantics by recognizing more meaning components of non root morphemes than usually acknowledged
we accelerated the process of lexical acquisition NUM by developing morpho semantic lrs which when applied to a lexeme produced an average of NUM new candidate entries
lrs offer greater generality and productivity at the expense of overgeneration i.e. suggesting inappropriate forms which need to be weeded out before actual inclusion in a lexicon
similarly destroyer as a person would be represented using the same event with the addition of a human as a filler of the agent case role
in reviewing the theoretical and computational linguistics literature on lrs one notices a number of different delimitations of lrs from morphology syntax lexicon and processing
figure NUM simulated annealing algorithm for word clustering
the schemes used are the pattern given in section NUM NUM and windowed training schemes with window widths of NUM NUM NUM NUM NUM NUM and NUM words
to determine the difference made by conceptual association the pattern training scheme has been retrained using lexical counts for both the dependency and adjacency model but only for the words in the test set
while his apl roach treats dialogue our targets are manual sentences
second an analysis model based on dependency grammar is substantially more accurate than one based on deepest constituents even though the latter is more prevalent in the literature
i have also shown that with a corpus of moderate size it is possible to get reasonable results without using a tagger or parser by employing a customised training pattern
the accuracy on the test set for all these experiments is shown in figure NUM as can be seen the dependency model is more accurate than the adjacency model
to implement them equations NUM and NUM must be modified to incorporate NUM in each term of the sum and the a factor of entire ratio must be multiplied by two
given the high frequency of occurrence of noun compounds in many texts this suggests tha the use of these techniques in probabilistic parsers will result in higher performance in broad coverage natural language processing
eight different training schemes have been used to estimate the parameters and each set of estimates used to analyze the test set under both the adjacency and the dependency model
if the probability estimate for pr t2 t3 is zero for all possible categories t2 and t3 then both the numerator and the denominator will be zero
our grammar produ ed l ossil le parse trees for NUM senten es
the feature structure that does not al pear i a sub structure appears in the corresponding core structure
note that the frozen goals are evaluated and the indices wdues have al prot riate values
intuitively the routing produces unified results for the part of fi instantiated by NUM NUM
our parsing method avoids this on line construction of phrasal signs by computing skeletal part of parse trees prior to parsing
rule r a rewriting rule without specific syntactic categories fs r a feature structure
the second is non termination of the execution of dcp in step NUM because of lack of concrete non head daughters
we have implenmnted our parsing metho l in common lisp ol je t systen
the la generated fronl the lexical entry for wrote in figure NUM is given in figure NUM
consider the case of executing append x b y in prolog
lexical analysis and named entity recognition ne the lexicon we used contains only syntactic information such as parts of speech and subcategorization frames
after identifying th e coreference relationships the pie unifies these properties associated with each instance of a domain entity and fills out the templates
more generally it must raise to t in finite clauses as well as infinitives
it involves tile following discourses NUM a mary looked at bill
one acc or ling to which negation should i1ol o
we reasoned that the subject who used a particular lexical 1at no time did any subject doubt that he she was interacting with an actual machine translator
ideally we would like users accommodation to a machine interface to be as high as possible so that the lexical variability of users speech can be as constrained as possible
recall that these conversations were unconstrained neither agents clients nor interpreters whether human or machine were under instructions to limit or modify their speech in any way
given that lexical accommodation is a real phenomenon then how can we characterize the patterns of accommodation for each experimental setting and what can we learn from them
in the hmnan human setting the agent used a significantly higher percentage of words first p NUM the client accommodated to the agent
since a causal relation which is shown by to or reba expresses a general rule the consequence can not include speaker s attitude like volition and request
consequently conditionals reba tara and nara since they occur less frequently than to in manuals and we have to collect more examples to estimate their property in nralluals
roughly speaking to and reba show causality relations namely some general rules and tara and nara are used in the case that the the antecedent is an assumption
the fact that not assumptions but general rules are usually described in the context of instruction is the reason why tara and nara are used less fi equently than to and reba
since tile constraints we pursue here are those which restrict the types of subjects we examined the correlation among the types of conjunctives the types of verbs and the subject
as result we obtained the following pattern of usage in matrix clauses NUM the connective particles to and reba have tt e same distribution of usage
the ru fonn can show one of tit followings speaker s volition speaker s request t o hearers or the action done hy a third party
therefore it is quite probable that not only the normal form but also some request form which is considered as a kind of wish appears in the matrix clause
we consider only the mood of the description of facts because manual sentences should describe only facts and must not in ludc sl eaker s at titude
the sentences having the mood are classified into two types tile lescription of an action and the description of a state like an expression for the ability of some action
in order to determine if one conversant was accommodating more than the other we examined the number of words used first by the client and the number used first by the agent
figure NUM key systems interrog clauses simplified figures NUM and NUM give the system networks of the komet
where do you want to go interrogative NUM sie wollen um drei uhr fahren
this article is concerned with determining the constraints on the selection of appropriate intonation in speech generation in human machine information seeking dialogues
we take into consideration factors such as dialogue history speaker s attitudes heater s expectations and semantic speech functions
latbis is highly relevant if the input channel is spoken since speech recognizers can not achieve a NUM recognition rate
the first two rules encode the course that the dialogue is expected to take while the other dialogue rules encode exceptions
in the present scenario this contextual knowledge is provided by the dialogue model reflecting the genre of information seeking human machine dialogue
promise the information knower can utter a promise when she wants to signal the information seeker that she is considering the request
the mood and key systems represent the grammatical realization of a move given that a move is realized as a clause
the ultimate constraint on the selection of features in the interpersonal semantics and grammar is the information located at the stratum of context
lagt pred je e le carrd est effacd pred carr6 the square is erased obj det pred le j interrogations three interrogative forms are met in french subject inversion fl est ce que questions f2 and intonative questions f3
on the contrary the criterion of relative completeness is deficient for most of the ellipses like t where the upper predicate to move is omitted n movc l the left door on the right too
in some cases a lexeme might however occur before its priming word a i want to enlarge the small window back priming situations are handled through the following algorithm evm c time a new word occurs NUM
it may be asked why gfice was not aware of the ihree generic aspects of dialogue partner asymmetry background knowledge and recta communication
grice proposes lhat the cp can be explicated in terms of four groups of simple maxires which are not claimed to be jointly exhaustive
this produces a lotal o1 sevcn dialogue aspects each of which is addressed by one or more generic principles table NUM
although important to sia s design specific principles may be less signil cant to a general account of dialogue cooperativity
gpi make your contrihution as inl ormalive as is required for the current purl oses o1 the exchange
then the system attempts to generate concise and unambiguous text for each action group separately
the content planner organizes the overall narrative and determines the linear order of the messages
though on the outset this phenomenon seems unlikely it does happen in our domain
this heuristics results in parallel syntactic structure and the underlying semantics can be easily recovered
the rank is an indicator of how similar an attribute is across the messages
the resulting message list after sorting each attribute is shown in fig NUM
once the distinct attributes are different from the combined messages the system starts a new sentence
NUM generating the most concise text it should avoid repetition of phrases to generate shortest
in this case the ordering for sorting is site equipment and then date
but such combination is blocked when a sentence becomes too complex
commas are not considered to be words or modifiers in the dependency model but they do give strong indications about the parse structure
we have shown that a simple statistical model based on dependencies between words can parse wall street journal news text with high accuracy
formally given a sentence s and a tree t the model estimates the conditional probability p t s
the same sentence is very unlikely to appear both in training and test data so we need to back offfrom the entire sentence context
english is a language with strong word order so the order of the two words in surface text will clearly affect their dependency statistics
first a constant probability threshold is used while building the chart any constituents with lower probability than this threshold are discarded
the other definitions in NUM are similarly redefined with pos tags only being used when backing off from lexical information
moreover once we have these links we need the same number between vp NUM and vp n categories since the first vp expansion simply unifies the subcategorization of the vdaughter with that of the vp mother
null whatever term one takes an important aspect of the relation is that it can be used to reduce the search space of possible syntactic analyses at an earlier point in parsing and thus serves to improve the efficiency of a parser
our version stated here tranfers the call to link from the definition of parse to that of leaf the motivation for this change steins from our use of top down information in the morphological analysis and in the treatment of missing lexical entries
the difficulties clearly lie in the last syntax rule vp or vp i and vp2 seems to be left recursive whatever we may mean by that at this point so perhaps no link arises at all from this rule
we suppose a price request to be represented by a feature structure more specific than the following speechact requestprice object obj j now the description of the objects may vary in specificity which makes it refer to many different objects
the predicate translatedifferences determines the relevant feature paths question is incompatible with the expected value an appropriate message should be communicated to the user along with the possibility to provide complementary information as well as to cancel the dialogue
in order to access all levels of representations this list is maintained for orthographic syntactic and semantic representations as well as representations of referred objects which allow the discourse blackboard to be seen as four database blackboards in parallel
by draw map picturename obj concrete NUM obj concrete or position x obj concrete 0c posltion i y
so far there have been two main approaches in this field for overviews on abstracting and summarizing see e.g. or
9this number corresponds in fact well to the observation of y that the opt ilnal smnmary length is bet ween NUM and NUM of the original document length
swords in the title and or appearing in t ln first last few sent enees all be given inore weight by tneans of an editable parame l e r
however it would have to be investigated how nmch weight increase the words from the user s query should get in order not to bias tile resulting output in too strong a way
the results indicate that it is indeed possible to build a system relying on a simple and efficient algorithm using standard tf idf weights only while still achieving a satisfying output rcb
the statistics for these smaller grammars are given below
bus and goes are a noun and a verb respectively
there are several notable qualities to these numbers
considering the simplicity of the approach we think these results constitute a proof of principle for the idea of treebank conversion
in addition it allows us to translate answers returned by the question into a more natural format for input to the decision tree models
consistency is the degree to which all team members posit the identical parse for the identical sentence in the identical document of test data
accordingly we present statistics below on the consistency and accuracy of expert h lm at parsing using the atr english grammar
the language provides easy access to word and tag nodes at any offset from the begln ng or end of the sentence
there is much farther that we can go in exploiting the information in the source treebank parse to aid in predicting the atr parse
another feature of the algorithm is that segmentation of the chinese input sentence is performed in parallel with the translation search
instead the btg itself can be used directly to probabilistically rank alternative alignments as described next
NUM whenever the segmentation preprocessor prematurely commits to an inappropriate segmentation difficulties are created for later stages
the translation lexicon was largely constructed by training on the hkust english chinese parallel bilingual corpus which consists of governmental transcripts
with regard to efficiency figure NUM demonstrates the kind of reduction that btgs obtain in the space of possible alignments
with each production of the grammar is associated either a straight orientation or an inverted orientation respectively denoted as follows
null the sbtg assigns a probability pr c e q to all generable trees q and sentence pairs
in practice a strong language model makes this unnecessary so we can instead optimize the simpler viterbi approximation
thus if we attempt a direct mapping of the pragmatic operators we might obtain a translation similar to the following NUM sort of difficult to reject something like that is this translation is quite awkward and does not fully reflect the pragmatic meaning of the original sentence
the database blackboards store a set of feature structures
the dialogue system is implemented as a blackboard system
however they do not adequately represent ambiguity
if so the remaining predicates are evaluated
figure NUM the rule serving to disambiguate com
it demonstrates how database access and rule application interact
we consider the rules shown in figure NUM NUM
figure NUM examples of a typed feature structure
figure priateness conditions used in the map application
for this reason the interaction is information driven
for example mr jordan of steptoe johnson is split into mr jordan and steptoe 8j johnson
ambiguity remains one of the main challenges in the processing of natural language text
in the following sections we describe how ambiguity is resolved as part of the name discovery process
cheaper models which include no context or world knowledge do very little disambiguation
this analysis of course can be over ridden by a name database listing beverly hills as a place
in addition naming conventions are sometimes disregarded by people who enjoy creating novel and unconventional names
this situation is the norm for dynamic applications such as news providing services or internet information indexing
of these nominator correctly identified the boundaries of NUM NUM NUM
if we employ a pure example based translation method most of the pragmatic information can not be reflected since it is not feasible for the example database to contain all possible pragmatically marked permutations of the examples
it indicates the theme and the theme of the utterance and places new or contrasting information into focus
before the experiments metho l corre t h rre i7
in case where both sentences have zero pronouns tile agreement disagreelnent depends on the context and doe s not have any tendency
mo lcl tim zca o pronoun rt solution with chc on x t
in this section we describe the experiments that our zero pronoun resolution method is applied to real japanese discourses to evaluate the effectiveness
the improvement of the i e rforma nce in method NUM also ilntflies tit plausibilil y of our method
since conjunctive postpositions of class c have no preference for the antecedents of zero pronouns the zero pronoun resolution is performed as usual
therefore it is necessary to extend the range of search for he antecedent to more previous postpartitione d
what is needed therefore is a proper investigation into the syntactic roles that punctuation symbols can play and a tbrmalisation of these into instructions for the inclusion of punctuation in n grammars
hence additionally their rules include the punctuation marks as distinct entities rather than cliticising them although they still require extra features to ensure proper application of the rules NUM
the bracketings were analyzed so that each node that has a puuctu ttion mark as its imme liate daughter is reported with its other daughters abbreviated to their categories as in
what is needed now is a thorough testing and evaluation of the suggestions made in this paper both against lmnctuation patterns from other corpora and in parsing novel material to maybe suggest better geimralisations
if text in the real world a newspaper for example were to appear without any punctuation marks it would appear very stilted ambiguous or infantile
it is more likely to be an item in a list that is introduced by a phrase such as views we v aired on the following matters
also since many valid rule patterns occur infrequently in the corpus there exists the possibility that there are further valid infrequent pmlctuation patterns that do not occur in the corpus
if based on the conclusions of these studies we are to include punctuation in nlp systems it is necessary to have some theory upon which a treatment can be based
it should also be noted that some incorrect category assignments were made at the earlier data analysis stages which explains why several of the revised rules have non phrasal level left most daughters
the simplicity of this helps users to add patterns easily although precise description of syntactic dependencies is lost
furthermore as stated in NUM the same person is likely to create different abstracts of the same text at different times
on the other hand this paper proposes a method for determining the wieghts of features by multiple regression analysis of correct examples which are abstracts created by testers
this abstract creation system is currently used in an informatioll navigation assistance system NUM as a tool for quickly viewing the contents of newspaper articles
to see how the speakers agree among themselves we further made a comparison between the speakers annotations which is summarised as below
in this paper we aim to investigate the quality of anaphors generated by the referring expression component in our chinese natural language generation system
we chose three rules termed tr1 tr2 and tr3 with different complexities among the possible candidates as the targets of the test NUM
the constraint of discourse segment beginnings in trp and tr3 and the salience constraint in tr3 would therefore have some effects on the output texts
as shown in fig NUM the anaphors in text NUM form a topic chain NUM within a single sentence
the speakers as a whole agreed with kappa greater than NUM NUM on NUM out of the NUM anaphors with complete agreement only NUM times
only two speakers agree with one another with a kappa value of more than NUM NUM none with a value of greater than NUM NUM
the second and the third rules have one additional constraint namely discourse segment boundaries and salience respectively added to their predecessors
the figures in the table show that the speakers do not achieve an agreement among themselves for the use of anaphors in this test
disambiguation algorithms recent word sense disambignation wsd algorithms can be categorised into two broad types NUM wsd using information in an explicit lexicon
NUM col NUM and NUM l level and NUM level format are explained in the anflex
finmlly a limited amount of lexicml normalization or stemming inay be f erlormed
we expect however filrther studies on pragmatic marks to enhance the parsing of these constructions
at last the preposition is assigned the tag argument of the introduced object
NUM otherwise one checks whether this word back primes some stacked ones
this selective approach led to significant results in some restricted applications atis
although this cooperation achieves a noticeable reduction of the perplexity it is however ineffective when the lfg parser collapses
an additional layer structural layer s handles furthermore the coordinations and the prepositions
their activities are propagated up to the output layer which corresponds to the primed words
these constructions are actually considered a peculiar coordination where the conjunction is missing de smedt NUM
this constraint is worked out by the recall of the already fulfilled microsemantic relations which were all previously stacked
in fact the semantics of the statement
datr a language for lexical knowledge representation
it can be improved in two ways
recall that paths are sequences of attributes
datr descriptions associate values with node path pairs
there are several points to notice here
and true true true if true false false
as it turns out different researchers have used it very differently
furthermore the grammar is required to obey what i refer to as the dependency constraint when a particular right hand side literal can not be evaluated deterministically the results of its evaluation must uniquely determine the remainder of the right hand side of the rule in which it appears
assuming that the original rule is defining the start category the query corresponding to the generation of the s john buys mary a book leads to the following seed magic s p0 p finite buys john a book mary
corresponding to the first right hand side literal in the original rule step NUM derives the following magic rule magic vp pl p vform csem ssem magic s p0 p vform ssem
suppose the original grammar rule looks as follows s p0 p vform ssem vp pl p vform csem ssem np p0 pi csem
after cycle removal incorporating relevant indexing and the collapsing of redundant magic predicates the magic compiled grammar from figure NUM looks as displayed in figure NUM figure NUM shows the chart resulting from generation of the sentence john buys mary a book NUM
an ordering is imposed on the set of frame slots such that inheritance decisions for slots higher in the order are conditioned on the decisions for slots lower in the order
in the event that a more likely theory exists then the more likely theory is selected but if no more likely interpretation can be found the unlikely interpretation is accepted
this formula is more noise resistant than NUM but produces generally less recall
backing off to independent semantic and syntactic probabilities potentially provides more precise estimates than the usual strategy of backing off directly form bigram to unigram models
each of these theories is combined with the corresponding prior probability p ft yielding p ft p t i ft
much work remains to be done in order to refine the statistical modeling techniques and to extend the statistical models to additional linguistic phenomena such as quantification and anaphora resolution
the probability p t is modeled by state transition probabilities in the recursive transition network and p w i t is modeled by word transition probabilities
these effects and preconditions are of crucial importance in reasoning about what the agent intends to do and what she presupposes
the relationship between an action and its decomposition specified by a recipe can be viewed as a phrase structure rule
given a goal a planning procedure searches for an action to achieve the goal a main action
state dependent interpretation a dialogue state is determined by the initial state and the effects of the i receding actions
an edge s aend value is bound to its end value if it is inactive
let us examine typical situations where the effects and preconditions of actions must be treated
in the extreme case ifp already holds the agent need not do anything
there are many kinds of dialogue phenomena that can be captured by such action enabling relationships
boy being a terminal element it needs no fnrther refinement but we do still need to specify its attribute
we are adhering to the view that it is better to start with such a generic lexicon and adapt it automatically with specialist words and senses
the significance levels of all differences are worth knowing but our main interest is the dis ference between a and b in recall and precision
sp spurious number of bracket pairs prodated by the parsing system that were not in the treebank but also do not constitute a crossing error
if the nmnber of eqnm answers would be extremely high the real test size ruay become too small indicating the test is meaningless
to show the difference between the usual evaluation and our evaluation method we give the results for two parsing systems we evaluated in the course of our research
of conrse this assumption simplifies the situation but it is closer to the truth than assuming the whole test can be modeled by a binominal distribution
we do not give the derivation but when doing the same for nn and combining the equations the following relation between m NUM and m2 holds
since japanese is an agglutinating language and words are not separated it is difficult to say what the words are in the first place
using the variation we find that the observed values are both extremely rare so we can reject the hypothesis that we are comparing two binomial variables
it is based on transferring constituency trees to dependency trees but that introduces many ad hoc choices and treebanks with dependency trees are hardly available
the regular test should include a nuinber of figures that we describe below which are much more informative than the usual bracket recall or bracket precision
null aside from the database blackboards there is one distinguished blackboard the discourse blackboard representing the discourse history
for example calcimine is defined in wordnet as a type of water based paint and is also found in roget s but does not occur in the bnc
this captures the intuition that the more frequent the observation of some events in a distribution the less likely it is that the unseen possibilities will occur
this trivial inference power is independently needed to deal for instance with NUM if np1 is a subtype of np then rule NUM will work only if trivial inference power is available when the sequence np1 vp is
consider the situation when the grammar has two ps rules for vps one for discharging a np np subcat list and one for discharging a np np pp list
the resulting ratios can then be converted to probabilities by normalizing them along with those for the attested entries for fax
the resulting fsm is not a markov model because probabilities on states represent output probabilities and not transition probabilities in the machine
for instance the result of applying the vehicle name verb of motion lexical rule can be input to several other lexical rules
for the hitting weapon and paint like substance classes this involved combining several roget categories and wordnet synsets
figure NUM shows some raw frequencies of noun and verb ed and ing form from the bnc
trans causative verb orth fax r syn result l k active fax rel sem event result ssig n
the word to word model maintains high precision even given much less training data
NUM suppose that categories r1 and r2 form a coordinate structure and NUM and NUM are the lengths of r1 and r2 respectively
other semantic features are more restricted to individual adjective senses
the additional features derived for s s s are shown in table NUM fn ln given family name nnp proper noun de de
if we could properly define a probability model NUM and calculate the likelihood value of each interpretation using the model we might also resolve ambiguities quite well
lexicon subgrammars clause level expressions and template patterns
i wish to thank the anonymous reviewers of my paper for their valuable comments and suggestions
table NUM accuracy of some hmm transducers for different languages
two tags with one or more barriers inbetween do not influence each
for every possible pair of a class and a tag e.g.
we computed equation NUM over various combinations of a and a after the model s first iteration over NUM aligned sentence pairs from the canadian hansard bitext
if u and v are not mutual translations then p u v tends to a very low probability which we will call a
if u and v are mutual translations then p u tends to a relatively high probability which we will call a
the competitive linking algorithm is more greedy than algorithms that try to find a set of link types that are jointly most probable over some segment of the bitext
thanks also to mike collins george foster mitch marcus lyle ungar and three anonymous reviewers for helpful comments
since it does n t store indirect associations our word to word model contained an average of NUM NUM french words for every english word
the simplicity of the competitive linking algorithm depends on the one to one assumption each word translates to at most one other word
neither ibm s model NUM nor our model is capable of linking multi word sequences to multi word sequences and this was the biggest source of error for both models
in tile figure the author has clicked on information and is presented with a list of the types of information from which document can be selected
knowle lge base in the middl of rcb m figure mmerlies the task model built by the x hni al au lhor
we now describe i i aftei lcb a technical authoring tool which supports the construction of tile task model discussed above and the drafting of multilingual instructions from that inodel
we found that technical authors stm t the documentation process by le rning how NUM o use the interface in question constructing a user oriented mental model of the product
they report however that task oriented help is beyond the capabilities of their system task oriented help would indicate why the user might want to perform any of the actions that are available
here the ui get mapped to the corresponding l just in case they end up between l lcb and ri in the output string
it maps the infinitive of those verbs followed by a sequence of subjunctive tags to the corresponding inflected surface form and vice versa
a replacement expression specifies that a given symbol or a sequence of symbols should be replaced by another one in a certain context or contexts
it is the union ot all single tq relations mapping all occurl ences of one ui empty and non empty to the corresponding
although our research is in the preliminary stage and tested with a small number of japanese stock market bulletins and their english the experimental results have shown a number of interesting collocations that are not contained in a dictionary of economic terms
stherefore a single rule in t can be mapped to as many as n NUM k rules in gt where n is the number of terminal symbols in t gt could be exponentially larger than t
our patterns on the other hand concentrate on specifying linear ordering of source and target constituents and can be written by the users as easily as NUM 9by sacrificing linguistic accuracy for the description of syntactic structures
the preposition to and de are used to specify that the patterns are for vp pairs and to be is used to show that the phrase is the be verb and its complement
these patterns can be associated with an explicit nonterminal symbol such as v or adjp in addition to head constraints e.g. leave v
then there exists a cfg gt such that for two languages l t and l gt accepted by t and gt respectively l t l gt holds
secondly lexicalization might increase the size of stag grammars in particular compositional grammar rules such as adjp np np considerably when a large number of phrasal variations adjectives verbs in present participle form various numeric expressions and so on multiplied by the number of their translations are associated with the adjp part
thus the first np np i in the source rule corresponds to the second np np i in the target rule the vs in both rules correspond to each other and the second np np NUM in the source rule corresponds to the first np np NUM in the target rule
translation of an input string s essentially consists of the following three steps NUM parsing s by using the source cfg skeletons NUM propagating link constraints from source to target cfg skeletons to build a target cfg derivation sequence NUM generating t from the target cfg derivation sequence the third step is a trivial procedure when the target cfg derivation is obtained
and our itclp language gives l t
hierarchy satisfied by NUM an t NUM
null we consider the following l ackground knowledge which is always true
o dismnbiguation by ti i p
m he bought the teles ope yesterday
logic programlning tcia NUM ulgu tge
bu g he gave the lnan the telescope c this morning
itowever as the result of the tagging and lemmatising process consists of feature bundh s implemented as dags the output format can be adapted very easily if required by defining various format filters
this simplifi fable NUM results of contextual tagging with an extensive tagset tagsetl versus a reduced one tagset2 on the eardio and neuro sets cation of the syntactic information greatly improves the results
iit that case if the most specific rule is fired the triggering of the more general rules is prevented
the control mechanism works with an agenda that contains the position of the words ill the input sentence
as a conclusion we believe that our t l performs relatively well and still has potentialities for improvement
the question whether these cases should be considered as bad or correct is left open the difference between the results is mainly due to the amount of unknown vocabulary around NUM for the cardio set vs around NUM for the neuro set which results in a difference of NUM NUM vs NUM NUM
any uumber of real channels may be marked sendiug or receiving
both types of channels can be configured in an dditional way
o1 the architc ctm m branch of the project
from word recognition to syntax seuumtics and fitmtly transfer
file interfaces or simple pipes are considered
null we assume that an application consists of a number of components
we describe experiences and results of the work on the first demonstrator
l urthermore multiple concurrent write attempts have to be synchronized
to circumvent this problem two analysts manually built the discourse trees for five texts that ranged from NUM to NUM words
for each discourse usage of a cue phrase we derived the following a regular expression that contains an unambiguous cue phrase instantiation and its orthographic environment
if a relation holds between two textual spans of the tree structure of a text that relation also holds between the most important units of the constituent subspans
unfortunately the linguistic community has not yet built a corpus of discourse trees against which our rhetorical parser can be evaluated with the effectiveness that traditional parsers are
a procedure that can be used by a shallow ana null lyzer to determine the boundaries of the textual unit to which the cue phrase belongs
we knew from the beginning that it would be impossible to predict exactly the types of relations and the sizes of the spans that a given cue marks
in our experiments we noticed at least for english that the best discourse trees are usually those that are skewed to the right
comparing these two approaches we adopt the former
twelve main conjunctive postpositions are investigated
the chart constructed during parsing supports both viterbi parse extraction and baum welch type rule probability estimation by way of a backward pass over the parser chart
the computation of probabilities is conceptually simple and follows directly earley s parsing framework while drawing heavily on the analogy to finite state language models
since predicted states only affect the derivation if they lead to subsequent scanning we can use the next input symbol to constrain the relevant predictions
pruning procedures have to be evaluated empirically since they invariably sacrifice completeness and in the case of the viterbi algorithm optimality of the result
however this may be impossible given that the probabilities can take on infinitely many values and in general depend on the history of the parse
the bottom line is that each application s needs have to be evaluated against the pros and cons of both approaches to find the best solution
more refined variations are possible the top level productions could be used to model which phrasal categories sentence fragments can likely follow each other
in finite state parsing especially speech decoding one often makes use of the forward probabilities for pruning partial parses before having seen the entire input
the signature for the function pkturename that returns a string for any given type that is as least as specific as obj zoncrete is given by picturename obj concrete string where a following or sign indicates whether or not the argument has to be defined when evaluating the function
we then remove all senses whose part of speech is not consistent with the one assigned by the tagger if none of the senses are consistent with the part of speech we assume the tagger has made an error and do not remove any senses
the in formation expressed as controls is never re l rred to by other events
the subject object arcs ensure that it is understood that farmers beat donkeys and not vice versa
NUM istributedness requires that one may read igl and NUM NUM independently front the other
semnet represents this as shown in figure l a
phis paper describes semnet the internal knowledge t lcb epresentation lbr loli i a i
only action nodes can be an action for an event node
b semnet epistemic event for roberto believes that every farmer owns a donkey
a fundamental principle of the design is that concepts are not reduced to primitives
cs cn is a necessary condition for this turn of communication to be successful
our understanding about it will hence help us understand basic workings of natural communication systems
each player attempts to maximize the expected utility over the entire compound game rather than for each constituent game
s and r hence do not just want to maximize the probability of successful communication
in fact the utility functions are probably equal because language use as a whole is a repeated game
in general several meaning games are played possibly in parallel during linguistic communica tion using a compound expression
a natural language meaning game is almost equivalent to the context of discourse which changes dynamically as the discourse unfolds
if s and r do not have common knowledge about the game this inference will constitute an infinite tree
such a case s is still intending to communicate a content c by way of making r recognize this intention
this assumption will be justified as a practical approximation in typical applications of signaling games and cheap talk games
guaranteeing that the lexicon entries and the rules are consistent we let everything unexpanded unless we are enforced to make structure explicit
the column semna shows the results for operating the sem parser in non autonomous mode that is simply verilying falsifying hypotheses fi om the syn parser
because hypotheses are uniquely identitied in our framework we must only send the integer that idenl ities tile falsified chart edge
only verifies hypotheses from tile other and doe s not generate additional hyl otheses tile overhead is neglectat le
an obvious choice for splitting up the grammar was to separate the linguistic levels strata such as syntax and semantics
since no syntactic eonst raints are involved in filtering we expect a considerable increase in processing time and number of hypotheses
the goal was to develop more flexible ways of using codescriptive grammars than having them applied by a parser with full informational power
to keep the communication efforts low only failures are reported back to the syn parser by sending simply the hypothesis identifier
unification based theories of grammar allow for an integration of different levels of linguistic descriptions in a common framework of typed feature structures
the support for a pair w riable label expresses how compatible is that pair with the labels of neighbouring variables according to the constraint set
iv n NUM his proposal i he a nalysis of sa involves
as we shml later see some of the data does not seem to square with this assumption
however this only applies if the focus np is not eml edded in a scope island
pulman has shown that higher order unifcation hou can be used to model the interpretation of focus
hence our i sv can he more accurately contpared to kratzer s presuppositior skeletort
it is assumed that the quantification domain of focus operators is a variable whose value is contextually determined
null second the hou approach permits an equational analysis which can naturally be nrther constrained by additional equations
since relaxation updating functions NUM NUM and NUM NUM need support values to be normalized we must choose some function to normalize compatibility values
the acts i.e. passive edges items in figure NUM resulted from semi naive bottom up evaluation ra ifor expository reasons some data flow information that does restrict processing is not taken into account
let s assume he wants to begin with event i
the approach taken here raises an interesting problem
this view raises a number of interesting problems a what shall we do if not all of the planned message can be expressed by the words available at a given moment
actually the choice of a specific word will depend to a large extend on pragmatic factors like speaker s goals time and space constraints hearer s expertise and so forth
yet it does not express exactly what was planned
all these words express the notion of movement
step NUM this could yield boy attribute
we construct a new program ping
step NUM suppose we decided to elaborate person
the first class symbol on the upper side and the first tag symbol on the lower side will be marked as an extension that does not really belong to the middle sequence but which is necessary to disambiguate it correctly
future research will mainly focus on this possibility and will include composition with among others transducers that encode correction rules pos null sibly including long distance dependencies for the most frequent tagging errors ill order to significantly improve tagging accuracy
we can divide a dialogue into smaller self contained units that provide information on what phases are over or yet to be covered are we past the greeting phase
when the parser returns an ambiguity in the form of two or more possible speech acts the fsm will help decide which one is the most appropriate given the context
in either of those cases the transition is determined by unigram probabilities of the speech act in isolation and bigrams of the combination of the speech act we are trying to disambiguate plus its predecessor
the first one is a general organization of tile dialogue s subparts the layer under that pro esses the possible sequence of speech acts in a subpart
the top level concepts of the grammar are speech acts themselves the ones immediately after are further refinements of the speech act and the lower level concepts capture the specifics of the utterance
let us take an example input and a possible parse for it NUM could you tell me the prices at the holiday inn request could you
the discourse module selects one of these possibilities
atr interpreting laboratories and project enthusias
or through modelling in a finite state machine
its robustness sin it can give an answer to problenls without an exact solution incompatible constraints insufficient data its ability to find local optima solutions to np problems in a non exponential time
the first one is that relaxation does not maximize the sui t ort flln tion but the weigh ted support for each variable so we are not doing exactly the same than a hmm tagger
sin e iimms lind the maxinmm sequ n e probat ility and relaxation is a maximizing algorii hm we an make relaxation maximize th se lllenc
corpus wsj wall street journal train 1055kw test 6kw tag set size NUM the interest of this corpus is obviously its size which gives a good statistical evidence for automatic constraints acquisition
the results l resented in table NUM are tit best overall results dmt we woum obtain if we had a criterion which stopped tit iteration f rocess when the result obtained was an optimum
although the most intuitive and direct scaling would be the linear function we will test as well some sigmoid shaped hmctions widely used in neural networks and in signal theory to scale free ranging values in a finite interval
athis is an issue that will require fitrtl er ati enlion since as constraints can be expressed in several degrees of g merality l he estimated probabilities may vary greatly del ending on how t he constraint was expressed
secondly we can observe that there is a general tendency to the more information the better resuits that ix when using btc we get l etter resuits that with b which is in turn better than t alone
this work was supported in part by an nsf graduate fellowship to the author and nsf grant iri NUM to paul smolensky and geraldine legendre
each cell of the table contains a partial description a part of a structural description and the harmony of that partial description
the mgorithm relies on linearizing the trees and then representing the complete database of trees as atrie structure which can be efficiently searched
hh ror tolerant matching of vertex list sequences requires an errol inetric for measuring how rnuch two such sequences deviate from each other
once we convert all the trees to a linear form we haw a set o vertex list sequences
we assume that all immediate children of a given node have unique labels and that a total ordering on these labels is defined
tile a st column gives the maxinnnn depth of the trees in rite t d ttal lso
no h cletions to a t bast equal y in iougth violating the dist mee constrmnt
additionally it is possible to state a semantic constraint on the resolution of multiple discourse relations which seems to prevail over the syntactic c command constraint
discourse relations in the verbmobil semantic construction japanese dialogues are analyzed within the same theoretical framework and with largely identical semantic macros as german ones
the first problem mentioned at the beginning of this section can be dealt with in this manner if only one discourse relation element occurs in a sentence
the mode predicate is applicable when multiple occurrences of predicates in one semantic class take a scope over any other scope taking elements together but the scope relations among each other are underspecified
for example in the current lud formalism it is assumed that a discourse relation has the widest scope among the scope taking elements in a sentence except for the scope of sentence mood
first if we keep the solution above discourse relation elements in the sentence are all candidates for the directly subordinated position to the top hole in a semi lattice structure
while scopal relations of quantifiers normally can be aligned scopal relations can but do not have to be built between discourse relations and between scope taking elements in general
the data fi om NUM and NUM present thus the empirical basis for a proper definition of o command in non linear obliqueness hierarchies
the antecedent precedes the reflexive but in NUM b it is the reflexive that precedes the antecedent in NUM b
meronymic relations in addition to the hypo hypernymy relation the better density would capture semantic relatedness and therefore better results can be expected
this work partially described ill agirre rigau NUM was started in the computing research laboratory in new mexico state university
we wish to thank all the staff of the crl and specially jim cowie joe guthtrie louise guthrie and david l arwell
figure NUM the semantic representation of the request
ea h of the action nodes in this sl rueture rel resent inter omw i e t complexes of procedural and descriptive instances
texts in the public domain sense tagged version of the brown corpus francis kucera NUM miller et al
when they are treated as input files we throw away all non noun words only leaving the lemmas of the nouns present in wordnet
in this paper we describe drafter an authoring support tool for generating user centred software documentation and in particular we describe how parts of its required knowledge base can be obtained automatically
the knowl tge base sut porl s he oilst ru l ion of he ask mo m discussed above
we decided to proceed in this way to determine at first how constraints in general are expressed
in such cases we must appeal to another source of control over the apparently available choices
ready reference in this genre m1 task elements are always realized through clauses
moreover it is not always clear from the text which type of constraint is expressed
however the function is accessed through the interface object and not through a plan
few modals are employed and when they are it is to express obligation impersonally
genre does so by constraining the selection of the task elements and the range of their expressions
we distinguished earlier two genres with which we are concerned ready reference and step by step
we summarise here the two genres that are strongly contrasted procedure and ready reference
both the tol down method and the proposed bottom up method gave the correct translation br the same NUM sentences with a success rate of NUM NUM
NUM i h fl somc laundry to be cleaned bul i ca n t remember where the clcaners is and i was wondering if you could help me
in the three subsequent sections we will explain a b and c focusing on the bottoir up and best first translation strategy
the words bus goes and chinalown are transferred to basu iku and chainalaun NUM respectively
in this combination x noun verb y matches the input string and is transferred to x wa y based on the result of distance calulation
null basu wa gozen i0 ji ni chainataun ni iki masu ik is the conjugated form of iku followed by masu a polite sentential final form
one case utilizes top down application the other case utilizes the new application method presented in this paper which adopts bottom up pattern application and retains only one substructure using semantic distance calculation
when a substring can be matched to the left part of a pattern and the right variables of the pattern are not instatiated an active arc is created for the substring
in tdmt translation is performed by applying stored empirical transfer knowledge which de null scribes the correspondence between source language expressions and target language expressions at various linguistic levels
the structure selected in c ontains its transt rred result and head word infbrination which is used for semantic distance calculation when combining with other structures
at NUM p m forex market trading was extremely quiet ahead of fnrther auto talks between japan and the u s slated for early dawn tuesday
NUM as depicted in figure NUM only expressions within a sentence are considered by introducing the explicit sentence delimiter NUM
if ejpn and cen q are good translations of one another a should be large and b and c should bc small
in contrast if the two are not good translations of each other a should be small mid baud c should be large
the variants include those in which the entire infinitival clause or a gerundive phrase based on it serves as the subject of the main clause with hard or right as predicate adjective but becoming more independent is hard for many children
the tendency toward sense specificity of nouns modified by target adjectives in these sentences is demonstrated by showing that there is little overlap in the set of nouns modified by the target in the two antonym co occurrence subcorpora for that target section NUM NUM
the old doctor and the young doctor rode in silence for two miles and indulged in their memories on the contrary these nuns young and old were invariably cheerful and happy almost gay and full of childish fun and laughter
katz principled disambiguation in many instances in the NUM sentence samples the noun modified by a target adjective was ambiguous with respect to one of the indicator attributes an indicator attribute did characterize some of the noun s common senses but not others
the most prominent example in our data is generalization from the non anaphoric it indicator for the not easy sense of hard which is also applicable for the not wrong computational linguistics volume NUM number NUM sense of right
with one exception wine the projected indicator nouns for the aged sense of old man people woman you he person lady and proper names of people refer to human beings
in other cases such as role nouns with more complex semantic structures we are able to resolve the semantic relation of adjective and noun but this ability can not be captured in rules as simple as those of table NUM
some indicator nouns were extracted not because the attribute applies to all senses of these nouns but because these nouns are used far more often in senses to which an indicator attribute applies than in those to which it does not apply
in contrast for each of the NUM out of NUM instances in which a correctness sense of right modifies a locational sense of side in right side of there is something explicit in the near context usually in the same sentence
a more complex example is provided by those indicators for the not long sense of short that are types of texts or utterances book manuscript monologue note phrase poem speech stanza story and syllable
proper names which are voted into more than one class are handled by choosing the highest priority class
where p represents the proportion of names within a tree node belonging to the class for which the tree is built
the date grammar is rather small in comparison to other name classes hence the performance for dates was perfect
p log2 p NUM p log2 NUM p NUM
delimitation is the determination of the boundaries of the proper name while classification serves to provide a more specific category
this paper presents an approach to proper name recognition that uses machine learning and a language independent framework
to acquire the knowledge required for classification each word is tagged with all of its associated features
feature translation occurs through the utilization of on line resources dictionaries atlases bilingual speakers etc
for non token languages no spaces between words it also separates contiguous characters into constituent words
only non verba m gorics sec linrichs and n tkaza wa
ilowever this derived le would have the undesirable consequence that it admits ill formed sentences such as NUM
the subsumption test for lexical rule application that we have argued for in this paper pertains to the first task
scntencc lina i posit ion for suhordim d c cla uses
ttowever these differences are entirely orthogonal to the theoretical issues discussed in this paper
informally speaking two feature structures are unifiable itf they do not contab incompatible information
iiowever the lass of argument raising verbs is not restricted to auxiliaries
4l ollowing abbreviatory conventions in hpsg the subscripted tags in pig NUM stand for the index va lues
NUM which in turn specifies for verbs under the features suili and comps the subject and non subject complements
b unsupervised training where information is gathered from raw corpora which has not been semantically disambiguated
the senses for a word in ldoce are grouped into homographs sets of senses realeated by meaning
null we attempted to solve this problem by making a slight change to the method for calculating the overlap
the optimisation is carried out by minimizing an evaluation function computed from the overlap of a given configuration of senses
this has been used extensively in nlp research and provides a broad set of senses for sense tagging
for each of the remaining words e of its senses are extracted from ldoce and stored with that word
our tagger currently consists of three modules dictionary look up module part of speech filter simulated annealing NUM
consequently there is no predetermined dialogue model that predicts the type or the information of the next utterance
a homonym consists of a lcxcme name a part of speech marker and a list of values of morphological features such as number case gender tense voice and so on
for that reason agreement errors give a high proportion of all grammatical errors in russian texts here and below the expression agreement errors means the use of words in incorrect forms
the parser intensively exploits the idea of syntactic preference used in a wide range of systems based on various principles see for example tsejtin NUM kulagina NUM NUM tsujii et al
a list of different graphic words corresponding to those homonyms was built on average it contained NUM NUM words and one of the words different from the initial word was chosen at random
in case c NUM l the sentence is regarded as correct and the process terminates in case c NUM l an attempt is made to improve the sentence
the general idea of correction can be expressed in these terms as follows for an input sentence which has c l minimal changes are considered that produce sentences with c i
the morphological and syntactic dictionaries which describe respectively the paradigms and syntactic properties of words cover about NUM thousand words the grammar does not cover a number of less frequent syntactic constructions
then a single random distortion was made in each sentence and the NUM distorted sentences were processed this was made twice with different series of pseudo random numbers used to generate distortions
let d he the number of homonyms of a certain fragment which do not belong to the initial morphs i.e. the graphic words of which are different from the words of the input sentence
as a result for each homonym of the initial morphs a set of variants is built i.e. a certain set of homonyms of the same lexeme that contains the given homonym
default NUM subject of sentence with tara or nara in a complex sentence with
it is a progressive substitution of the nl terms located in the syntactic tree with concepts and templates of the conceptual representation language
we propose to exploit its main advantages in order to build our anaphora resolution mechanism extending it to deal also with intrasentential antecedents
each ir in the algorithm appropriate to an anaphor suggests one or several antecedents depending on the focus and on the anaphor type
thus it could be set up in any conceptual analyser as long as a semantic representation of the text is available
the conceptual analyser s strategy consists of a continuous step by step translation of the original natural language sentences into conceptual structures cs hereafter
our algorithm fails in resolving his in NUM because the algorithm searches only for the entities that precede the anaphor in the text
intrasentential antecedents i.e. antecedents occurring in the same sentence as the anaphor are a crucial issue for the anaphora resolution method
the main idea of the methodology is the use of other kinds of restrictions between the anaphor and its antecedents than the syntactic ones
consider the algorithm the expected focusing algorithm is applied to the first ee eel which contains non prr anaphors
this relies also on the fact that the subject of the initial ee can not be a pronoun second hypothesis
the statisl ical lal a r each relationshi l are shown in figure NUM
step NUM specification design the a ua lysis taught us tllaj
users wa nt t o use l he tuouse a mbiguously
expressi ms to tlm a pplica tio is collected
the include design and editiug pt esenta t
one is bh ckboard a rchitecture where a gen ts excha nge
NUM prefer a pattern p with a source cfg skeleton to any pattern q that has fewer terminal symbols in the source cfg skeleton than p
for each noun phrase the system determines whether the noun phrase was a subject direct object or prepositional phrase based on the syntactic analysis produced by the sentence analyzer
but in the joint ventures domain good extraction patterns often require both verbs and nouns e.g. x formed venture is better than x formed
NUM if for no other reason a human would be required to assign semantic labels to each definition so that the system can identify the type of information that is extracted
therefore this concept node would be activated by phrases such as x was murdered x and y were murdered and x has been murdered
in contrast autoslog ts simply allows all applicable heuristics to fire s often producing multiple extraction patterns of varying lengths and lets the statistics ultimately decide which ones work the best
a signature consists of a concept node paired with the word that triggered it although in the experiments presented in this paper there is a one to one correspondence between concept nodes and signatures
the underlined word represents the trigger word the bracketed item represents the type of information that will be extracted by the concept node and the remaining words represent the required context
for example an information extraction system for the terrorism domain might extract the names of perpetrators victims physical targets and weapons associated with terrorist events mentioned in a text
we conducted a series of experiments with autoslog ts to evaluate how well it performs on a text classification task and to assess the viability of using it for information extraction tasks
the relevancy signatures algorithm uses a relevancy threshold r to identify the most relevant signatures and a frequency threshold m to eliminate signatures that were seen only a few times during training
theorem NUM shows that the syntactic coverage of t is in general only computable by t itself even though t is merely a cfl
the results of this experiment are shown in table NUM
q ony broke the glass to NUM ieces
following saint dizier s work we construct n ary syntactic characterizations
NUM group the verbs according to their syntactic signature
in every case the median is higher than the mean
note that the pp node is marked with its head preposition
in this experiment the sets of verbs with a high overlap index are of interest
as an example of how the word sense disambiguation process and classifcation consider the non levin verb attempt
the difficulty then is to disambiguate and classify verbs that do not occur in levin
for these different strategies we see the percentage of perfect overlaps as well as both tire
figure NUM five examples of ironic utterances
i ve expected a clean room
NUM NUM ironic environtnent and its inlplicit display
component holds in a current situation
e i m disat t ointed with the messy room
b mayl e the NUM each is crowded with people
this low level implementation is only known by the predicates that make up the interface
any tlepcudcncy parse ca n i c built ill hy eovered coitca tena i ion
however tables NUM NUM show pilot results for a small set of data drawn from that corpus
we have undertaken a careful study to compare these models success at generalizing from training data to test data
lqgure NUM result hit in a double subject for jni and eavitlg of childless
figure NUM a a bare l ones dependen y parse
also matters are complicated slightly by the probabilities associated with the generation of stop
l h choice of l he right model is not a priori a vious
wit h other analyses if so the parser disca rds all but the higlmsl scoring one
data structures are again updated by a call to procedure update with the second parameter equal to NUM then state qs is associated with node m37 the root node of c3
since the root of lhs rz i.e. node n15 belongs to q8 mz passes the test in the head of the for statement in the main program
to simplify the presentation we first make the assumption that the order in which we apply several instances of the same rule to a given tree does not affect the outcome
sas we noted earlier an mternative would be to employ an agglomerative mgorithm
based oll the co occurrence data using 8ollle sill ilarity distance measure
consider the se ond senten e tom also likes apples in figures NUM mm NUM ill this sentence the scope of also can NUM e to m likes the entire predicate the whole sent enee
the number of overparsing passes through the block is bounded from above by the number of cell categories due to the fact that overparsing cycles are suboptimal
the first consequence of this is that the overparsing operations must be considered after the underparsing and parsing operations for that block
all possible ways in which the factor categories taken in order may combine to cover the substring must be considered
in the description of the algorithm given in section NUM NUM each repeat until loop considers the overparsing operations for a block of cells
when the table has been completely filled cell s NUM j will contain the optimal description of the input and its harmony
in general any partial description covering any substring of the input may be extended to cover an adjacent input segment by adding that additional segment marked as underparsed
thus the optimal unfilled structure for each non terminal and in fact each cell category must be determined for use by the overparsing operations
the first type of parsing operation involves productions which generate a single terminal e.g. p p
null for our work we use the sense definitions as given in wordnet which is comparable to a good desktop printed dictionary in its coverage and sense distinction
we tested our wsd program named lexas on both a common data set used in previous work as well as on a large sense tagged corpus that we separately constructed
however their results show no improvement in fact a slight degradation in performance when using surrounding words to perform wsd as compared to the most frequent heuristic
since the word interest appears as a noun in this sentence lexas will only consider the noun senses of interest but not its verb senses
as the starting point for discussion we take the grammatical systems of mood and key for they grammatically encode semantic speech function and speaker s attitudes and lead directly to selections in tone
in the following section we determine the kinds of information that are needed in addition to what these resources provide and suggest a method of integrating the additional resources in the overall system
NUM yet in the context of generating speech in information seeking dialogues where intonational features are often the only means to signal a dialogue act these aspects have to be taken into account
jal anese document classification on words needs a high l recision japanese morphological analyzer and a great amount of lexical knowledge
to find out the closest domain we measured an angle between the unclassifted document and the NUM domains in the feature space
suppose we denote the frequency of kanji i in the domain j mid and we assume that kanji i is distributed evenly
then we classified japanese documents into domains by mea suring the similarity between new documents and the domains in the feature space
NUM the featllre vect ors of the doliiains are obtained by the inforniation oll donlain specilic kanji characters and its fr0qllolioy of occllrrellce
we assumed that these significant kanji characters appear more frequently in one donlaii i than the other and extracted theni by the x NUM method
in contrast the style of the editorial articles and scientific american in japanese is similar to that of a thesis
summary the above discussion is summarized in table NUM
but it is signilicanl i hat we can avoid the cost of iriorphologi at mialysis which is not so perfect
all possibly underspecified feature structures of type NUM with NUM e o that have been added or non monotonically modified since the last stop of the inference procedure are looked up in the discourse blackboard
each sense was usually represented by several clusters
a concrete example will serve to illustrate
all phones are given in arpabet
note that the entire alphabet comprises NUM symbol pairs
we can summarize this discussion more formally as follows
table NUM explanation of symbols in figure NUM
each leaf node represents a single rule
the project s goals are to implement a verb phrase ellipsis resolution algorithm automatically test the algorithm on corpus data then automatically evaluate the algorithm against human generated answers
rather we mean that the agent does n t realize that there is a choice point it is used in two situations when c is totally accidental as in NUM be careful not to burn the garlic
however as will see we still need two different algorithms in order to determine which items are in focus in the target sentence in mt
we must check to see if they are discourse new information as well as checking if they are being contrasted with another item in the discourse model
monotonic multiple inheritance networks are most naturally used to represent generalisations over the properties that groups of linguistic objects share inspection of any network will eonfirin that they are usually deployed to express what is essentially a componential analysis of objects and of the relationship between them defined on the basis of this analysis
for the purposes of experimentation a grammar fragment was iml lemented in the alei system a lean formalism with a simple inheritance type system and a siml e context free rule backbone
to process a trivalent version the parser will backtrack on the bivalent version will use a lexical rule and then it will either succeed or it will backtrack again and use a second lexical rule
in this paper we explore an alternative to horizontal relatedness which exploits the idea that it is often possible to conceive of the linguistic objects in such a way as to eliminate potential sources of ambiguity and additional external mechanisms
in fact pollard and sag also refer to declension class membership and similar facts as horizontm relations and as we shall see the boundary between vertical and horizontal relations is not immutably fixed once and for all
the lexicon will then contain one verbal entry and the system will rely only on the existing resources the type hierarchy to provide the different interpretations of the predicate which license the distinct eoml lementation patterns
13ut this is only possible once one frees oneself from a view of lexical relatedness as something which holds essentially between words objects which correspond to maximal types that is types at the bottom of the type hierarchy
mentioned just in the previous sentence and postpose the rest of the discourse old items liowever the conditions for dropping arguments can be very complex
in NUM the numbers following t and f indicate the step in the respective algorithm which determined the topic or focus for that sentence
NUM believe that the order in which speakers place old vs new items in a sentence reflects the information structures that are awdlable to the speakers
n the information structure is that i use for turkish a sentence is first divided into a topic and a comment
broad wide focus focus projection is also possible where the rightmost element in the phrase is accented but the whole phrase is in focus
now let me get back to the last candidate to run as it raises an interesting problem
table NUM compares different transducers on an english test case
we do however not estimate probabilities over paths
however as shown in section NUM NUM the additional estimates of bigrams and trigrams will use the context to select a more appropriate tag
our algorithm addresses this problem in two ways
i.e. semantic categories which prevent incorrect combinations of concrete linguistic resources during surface realisation
this does not mean that controlled languages should be considered as absolute references
the dependency tree is enriched with communicative bipartitions such as theme rheme and given new
in the procedures we have analyzed only the operl function seems to be relevant
a key problem for text generation is to be able to avoid such incorrect sentences
let us now describe briefly how these functionalities are concretely integrated in the lexicalisation component
the choice of operator verbs is often a consequence of technical writers stylistic preferences
here are some excerpts which illustrate this regularity 2e bleed suction lines
however it is by no means an unattainable task if we structure and organise our analysis lexicons in such a way so that the information they contain can be used at best for building generation lexicons
moreover with this technique we can produce multifingual generation lexicons by lexicafising the concepts of the reversed lexicons in different languages this ensures that we will have a lexical item or phrase for lexicallsation available
a multi purpose knowledge base since building computational semantic lexicoas is a very time consuming task we should aim at lexicons which conform to the three following conditions a multi lingual french engfish japanese russain spanish etc format of the lexicon b multi rr d
from a theoretical point of view regenerating the source text with the reversed analysis lexicon enabled us to enhance several issues as diverse as evaluating analysis lexicons testing the semantic analyser evaluating which information should be added to the generation lexicon and testing the grain size of the pivot point between analysis and generation
we only show partial entries for superentry of the concept acquiiie as shown in figure NUM
NUM tic information for natural language processing phonological information essentially for speech recognition and production structure of the lexicons c multi use so that they can be used for analysis generation mono multi lingual mt or speech processing
in this experiment we considered only the word chunks thai appeared more than NUM times for fixed collocations and more than NUM times for flexible collocations
given the scope of bevin s work it is not easy to verify the central thesis
NUM reseilte l in section NUM
the union of the syntactic patterns corresponding to these sentences forms the syntactic signature for the verb
because of the insta n iation
the role of lexieal information in supporting inferences
these features include both grammatical and prepositional case
figure NUM drs of versehenke n
two pieces of information for the seleetional restrictions
karat md l lcb ob h uts her 994a kaml and l lcb obde uts her
acc trding to the itrevious context
NUM everything possible in NUM cg is a lso possible in mm i cg
llulllbers of input strea ms while NUM cg ca n receive only one
ea ch mode processing is ea sily ma pped to a n independent a gent
dra wing l ool developinenl a iong with multi modal method is re pori ed
l usion covers the possil le combination of different types of data
thus attempt falls under case NUM b of the algorithm and a new class is hypothesized
if the chi l says ls this points a t elel hant bigger than this points ac pteranodon
unfortunately the situation is made even luore conlplex by the fact that the system ca nnot
the synonyms for each of these classes have the following ldoce encodiugs respectively NUM i i foil i on
generating intersection c w f c ew from c w and c ew is not easy because the procedure ofpairingjw c c w and eu e c ew is nondeterministic
while selecting the minimmn of the two approxinmte wducs is safer it does not guarantee a precise value
accordingly the feedback of extracted pairs will probably improve performance even though some of them are erroneous
the method is divided into three parts japanese text processing english text processing and bilingual processing
of cource some of the correspondences between the co occurrence sets may be also missing in the bilingual dictionary
one exception is an english np starting wilh a noun that is included in an np starting with an adjective lmcause the case of an adjective modil ying a nominal compound is just as likely as the case of an adjective being a part of a no illinltl c o m pc ulld
moreover if we treated them in the same nlanucr its co occurrence relations it would cause some confosion
i i english text the two inputs to the addres comparato coincide with each other a lock identification number register an identification numberlcomparato and an and gate
ostia takes as input a training set of input output pairs
the other biases phonological rules to apply across natural phonological classes
these verbs are taken from i e translations provided in bilin english glosses
in some cases the verbs were seanantically unrelated and consequently the mat ping from syntax to semantics was muddied
this paper addresses the issue of word sense ambiguity in extraction from machine readable resources for the construction of large scale knowledge sources
as we saw above word sense disambiguation is critical to tile success of any exical acquisition algorithm
the semantic extensions are sets of verb tokens and likewise the syntactic extensions are sets of verb tokens
the outline of the verb based experiment is as follows NUM automatically extract syntactic information from the example sentences
this model exploits the following pragmatic constraints
filler terms such as gto were produced to satisfy the time constraints
when pragmatic constraints were used this implemented systein generated relevant discourses
figure NUM part of transcription of dialogue
serts an utterance to provide acknowledgnmnt
figure NUM discourse relations in figure NUM
table NUM frequency distribution for information units
moreover any object marked as a topic becomes a focused one
a liscourse segment is an iu or a sequence of ius
pt NUM NUM NUM NUM NUM ktu ti b st it finally measures NUM and NUM are derived by prefixing lcb tu rcb to measures NUM and NUM respectively
note that one cmmot place b and b i in llc and i lc respectively as tilt ease in NUM NUM because the parsing function cuts into the first syllable
finally rule r6 simulates the syncoi e rule in NUM note hat v ill ls must unify with v in lex ensuring that the vowel of the affix has the same quality as that of the stem e.g.
verb affix measure NUM verb af f ix measure g verb affix measure lo the first column indicates the tape on which the morpheme sits and the second column gives the morpheme
discourse markers are ambiguous with respect to the rhetorical relations that they mark and the sizes of the units that they connect
for dutch a prototype covering a larger part of the dutch grammar and medical vocabulary is under development
discourse markers are used in a manner that is consistent with the semantics and pragmatics of the discourse segments that they relate
for each potential discourse marker we then used an automatic procedure that extracted from the brown corpus a set of text fragments
first and most obviously some deterioratkm in quality is to be expected given the relatively impoverished linguistic base we start with
the first sentence in this fl agment introduces three discourse referents bearing different grammatical functions none of which appear in subordinate contexts
lappin and leass algorithm for pronominal anaphora resolution is capable of high accuracy but requires indepth full syntactic parsing of text
what is worth noting is the small number of errors which can be directly attributed to the absence of configurational inh rmation
implementing a gender dis agreement filter is not technically complex as noted above the current algorithrn contains one
for NUM the company set up its headquarters in hall NUM the newest and most prestigious of cent s NUM halls
be possible to increase the overall quality of our output bringing it much closer in line with lappin and leass results
the higher salience of the optimal candidate which ix also a member of this coref class shows the effect of the locality heuristic described in section NUM NUM NUM
in contrast to that work our algorithm does not require in depth full syn tactic parsing of text
the focusing algorithm updates the state of the focus after each sentence anaphor except the first sentence
sidner proposed a methodology modelling the way focus of attention and anaphor resolution influence one another
we made two kinds of algorithm evaluations the evaluation of the implemented procedure and an evaluation by hand
we comment here on the main aspects of the conceptual analysis that are related to the anaphora resolution process
an important aspect appears when one designs a concrete system namely how to make other disambiguation processes cohabit
at a certain point when the gdm calls the anaphora module to deal with a given anaphor the status of the conceptual analysis could be charaeterised by the following parameters the set of conceptual structures for the current reading ri on which the resolution is performed given that several readings could arise from previous ambiguity processing
for example the sentence to say that they agree to form a joint venture is represented in a simplified way with three templates corresponding to the predicates move information from to say produce an agreement from to agree produce a joint venture from to form
NUM three of the world s leading advertising groups agence havas s a of france young rubicam of the u s and dentsu inc of japan said they are forming a global advertising joint venture
in this process one step is missing we namely wanted to remove trivial brackets before evaluating
this enables a receiving component to select messages oll individual channels
the failure of these predictions is illustrated in NUM which presents the instanciation of schemata NUM
binding theory predicts that binding constraints on subcategorized elements may change by virtue of the application of lexical rules
to model this integration we utilize a unification operation over typed feature structures carpenter NUM NUM pollard and sag NUM calder NUM king ssee wahlster NUM for discussion of the role of dialog in resolving ambiguous gestures
t can l e noticed that ea h transition arc of tim coral ileal l as can be seen as a rewriting rule in cfg or a dott ed notation in a chart parser
by head dtr or non head dtr and i for a ty p g paths rid f there is p e path s n f which prefixes p
definition NUM lexical entry auton aton la a lezical entry automaton is a tuplc q a qo rcb whel e q a set of states where a state is a quasi sign o
NUM phase NUM parsing the algorithnl of phase NUM parsing is given in cursive NUM rocedure which takes an edge as input and builds ul sub structures which is differ ential feature structures representing modifications to core structures in a bottoln u NUM nlanner
the table entries are weighted and the result of this phase is a list of candidates sorted from the most to the least efficient for the current goals
without an appropriate preliminary statistical analysis to make the important points stand out and without an effi NUM ent organization and presentation the reader might be lost
in figure NUM the message is totally different and corresponds to a different goal to compare the profits for the NUM years of the data set
the example presents the same set of data profits during the years NUM NUM according to two different perspectives which reflect the writer s goals or intentions
compare two variables show the evolution of a set of variables and the system generates a report in igtex with the appropriate postscript graphic files
instead all of its knowledge is encoded in the links and weights of the table which was first created using a set of graphical rules and conventions
these annotations indicate the types of the variables how to determine the relational keys for the data and a series of predicates describing the writer s intentions
relational keys are similar to the notion of the same name in relational databases NUM and help determine which variables depend on which others
we used the valid clause boundaries assigned by judges as indicators of discourse usages of cue phrases and we determined manually the cue phrases that signalled a discourse relation
in this paper we show how one can find and exploit approximate solutions for both of these problems by capitalizing on the occurrences of certain lexicogrammatical constructs
then we try to find a inaximm subset of conslraints in the se ond strongest level with respe l to t he union of the
tlow many semantic features are covered
the alternatives are represented inside curly brackets
this includes only those syntactic categories for which we found more titan one example in the d tta set for this reason the percentages do not total to NUM
french see figure NUM shows a strong preference for the use of the two forms of imperative imperative simple and imperative infinitive the infinitive anti tile gerundive
generating good quality draft instructions requires a detailed specification of how to map from semantic representations of the task actions onto a wide range of linguistic expressions
in the case of avast and apr followed by a nominal there is a strong preference for placing the prepositional phrase containing the nominal first
one is identical in form to the infinitive of the verb and is usually associated with a generic addressee a public form of address
first two syntactic forms infinitives and imperatives dominate together they account for over NUM of the action expressions in tile data set
our overall approach is to obtain different language drafts that are congruenl with the technical content embodied in the task to be performed and with other relevant information about the task
english appears the most permissive in terms of both overlap between ed and ing bearing expressions and lack of influence of ordering a combination of the characteristics of the other two languages
our analysis shows that selection of syntactic expression and local discourse relation strongly interact and provides a rather clearer picture of the influences that bear on the mapping front semantics to syntax
speechact showpath after having processed the utterance show me how to get to the museum with a misrecognition on the museum
this approach integrates a diverse set of knowledge sources to disambiguate word sense including part of speech of neighboring words morphological form the unordered set of surrounding words local collocations and verb object syntactic relation
we would like to thank dr paul wu for sharing the brown corpus and wall street journal corpus dr christopher ting for downloading and installing wordnet and semcor and for reformatting the corpora the NUM undergraduates from the linguistics program of the national university of singapore for preparing the sense tagged corpus and prof k p mohanan for his support of the sense tagging project
instead of tagging every word in a running text as is done in semcor we only concentrate on the set of NUM most frequently occurring and most ambiguous words and collected large enough training data for these words only
attention to be given NUM activity subject etc NUM NUM which one gives time and attention to NUM NUM advantage advancement or favor NUM a share in a company business etc
to our knowledge this is the first time that a wsd program has been tested on such a large scale and yielding results better than the most frequent heuristic on highly ambiguous words with the refined senses of wordnet
phological form NUM unordered set of surrounding words NUM local collocations and NUM verb to the left verb object syntactic relation we conducted NUM separate runs of NUM random trials each
note that the formulation of this rule does not make any domain specific assumptions except that there is a type obj path that carries a feature dst
this linguistic theory offers many potentialities for multilingual applications
NUM the other classes nodes atoms and variables must be distinct and distinct from the reserved symbols but are otherwise arbitrary
irregularity can be treated as just the limiting case of subregularity so for example the morphology of do can be specified as follows 1deg
inheritance via the empty path is ubiquitous in real datr lexicons but it should be remembered that the empty path has no special formal status in the language
this approach has the advantage that the attribute ordering used in the mor paths is handled internally the leaf nodes need not know or care about it
tree structured networks as their name suggests allow any node to inherit from at most one other node so multiple inheritance conflicts can not arise
because datr deals in functions it does not embody any notion of disjunction or any possibility of multiple values being associated with a single node path pair
this problem is overcome in datr in the following way such exhaustively listed path value statements are indeed present in a description but typically only implicitly present
sample analyses of phenomena such as inflectional syncretism and verbal subcategorization are given that show how the language can be used to squeeze out redundancy from lexical descriptions
datr is a simple spartan language for defining nonmonotonic inheritance networks with path value equations one that has been designed specifically for lexical knowledge representation
the maximum likelihood estimator in NUM is likely to be plagued by sparse data problems c j lcb j wa lcb h aj h i may be too low to give a reliable estimate or worse still it may be zero leaving the estimate undefined
we have developed a heuristic distance measure which takes several such features into account the current distance measure aj h is the combination of NUM features or questions we motivate the choice of these questions qualitatively section NUM gives quantitative results showing their merit question NUM does the hjth word precede or follow the jth word
for example consider the relationship between sales and the three tokens of of example NUM shaw based in dalton ga has annual sales of about NUM NUM billion and has economies of scale and lower raw material costs that are expected to boost the profitability of armstrong s brands sold under the armstrong and evans black names
for example for figure l a example NUM s smith ggp president nn of in ibm nnp announced vbd resignation n n yesterday n g sections NUM NUM to NUM NUM describe the dependency model
for example wl smith in this sentence 4the triple can also be viewed as representing a semantic predicate argument relationship with the three elements being the type of the argument result and functot respectively
four configurations of the parser were tested NUM the basic model NUM the basic model with the punctuation rule described in section NUM NUM NUM model NUM with tags ignored when lexical information is present as described in NUM NUM and NUM model NUM also using the full probability distributions for pos tags
the key to the statistical model is that any tree such as figure l b can be represented as a set of basenps NUM and a set of dependencies as in figure l c
it s sometimes hard for a motorist to pass a young fellow standing on the edge of a highway
much thought had gone into that costume and it seemed just right for a poor man s wife
these indicate the not young sense of old and the not long sense of short
co occurrence counts for each of the ten antonym pairs are given in table NUM of the appendix
these attributes being semantic must relate in fact to noun senses and not to nouns per se
thus this section is concerned with the nature of underlying relations not with formulating a disambiguation procedure
some are readily characterized in terms of the general approach of the previous section others are more complex
table NUM shows that these few general attributes cover almost three quarters of all instances of the target adjectives
the target adjective light modifies the pronoun it which refers anaphorically back to john s justeson and slava m
another widely pertinent example is the more complex ambiguity in the reference of modified nouns for roles or relationships
this underspocification corresponds to the lack of a precise definition of the expression utterance a term always used but intentionally left undefined NUM
for resolving an anaphor in the first clause of un propose the dements of cy un NUM in the given order
incremental parser generation for tree adjoining grammars
in my contribution evidence for a mixed mode strategy is brought forward which favors a particular set of sentence internal antecedents given by functional criteria
each of these sentences is processed by our algorithm in linear order one clause at a time with the usual centering operations
the later turning on of the computer it resumes working at exactly the same place
which will then provide this theorem cherry sem glosses lcb sweet red berry with pip tree bearing sweet red berry with pip wood from tree bearing sweet red berry with pip rcb
next we sumnmrize how flexible collocations are extracted
that is eoineidenee delineates candidates for usefifl expressions
way and comhined statistics with a bilingtta l
which form more thaal ha if lhe candidates
we call hereafter this type of collocation fixed eolloeatlon
the other direction for extracting bilingual cob local ions
growing availability of lm ge textual cort ora and the in reasing number of applications of colloeal ion extra tion has given risc NUM o wu ious apt roaches on the i opi
we then formalize the dependencies between case slots as the probabilistic dependencies betweeit these ralldoiil variables
we then formmize the dependencies between case slots as the probabilislic dependencies between these random variables
lata itsillg pa ttc n matching tccl t iqucs
figure NUM shows the results of these experiments for these three artificial models averaged ower to trials
finally the problem of how to determine obligatory optional cases based on dependencies acquired fi om data
thus our proposed method can in effect discover implicit word senses fi om corpus data
we randomly selected NUM verbs among these 35r verbs and attempted to acquire their case frame patterns
punctuation marks which then are sent through a morphological analyzer that using a lexicon NUM produces primitive frames for the segmented words
we try to bridge the gap between the typically hardto scale hand crafted approach and the typically large scale but context poor statistical approach for unrestricted text parsing
we call the gap before the ith word gi a sentence with n words has n NUM gaps
both element nouns are highly ambiguous with respect to german but the english compound conclusively maps to the german compound zinssatz
section NUM describes a wide variety of datr techniques including case constructs and parameters boolean logic finite state transduction lists and dags lexical rules and ways to encode ambiguity and alternation
for instance the object level of the discourse blackboard can be seen as database for anaphora resolution when the representation of an anaphor is added to the semantic level of the discourse blackboard the object level of the discourse blackboard is considered to be a standard database blackboard and the antecedents are determined
instead of each word contributing one we normalise it s contribution by the number of words in the definition it came from so if a word came from a definition with three words it would add one third to the overlap total
even in the cases where data with the appropriate sense distinctions is available the text is unliicely to be from the desired domain a word sense discriminator trained on company news text will be much less effective on text about electronics products
at its best it disambiguates both verbs adjectives and the nouns they modify at the same time but we shall use this information late in the disambiguation process when we hope to be reasonably confident of the senses of nouns in the text from processes such as NUM NUM and NUM NUM
although it may be used for decreasing the number of ambiguous readings of
the total number of rules is NUM part of them being combined rules
adjectives are grouped according to whether they t ake class prefixes or not
in it conditions for the application of tile rule are defined in detail
the grammatically correct way of disambiguating it is by referring to the following word
n w number of unique word forms amb t
it is assumed that the solutions suggested here apply mso to other bantu languages
the morphological analyzer swatwol was so designed that it would be ideal for further processing with cgp
the parser performed best with newspaper texts leaving ambiguity to NUM NUM of tokens
the percentages in table NUM decrease rather systematically the more readings a word form has
NUM to avoid having arcs in the pruned automaton leading to such identical entries we use a tabulation method NUM note that the order in which two lexical rules are applied is immaterial as long as both rules modify the value of different features of a lexical entry
in order to be able in the following steps to remove a transition representing a certain lexical rule application in one sequence without eliminating the lexical rule application from other sequences every transition except those introducing cycles is taken to lead to a new state
assume the signature in figure NUM on which we base the example throughout the paper and suppose the lexical rule specification shown in figure NUM NUM this lexical rule applies to base lexical entries that unify NUM with the in specification i.e. lexical entries specifying b and y as
with respect to processing the extended lexical entry of figure NUM is problematic because before execution of the call to q l it is not known which information of the base lexical entry ends up in a derived lexical entry i.e. tag is completely uninstantiated
such a transformation however would result in the loss of a representation of the lexical rule predicates that is independent of a particular word class but an independent representation of lexical rules constitutes an advantage in space in case lexical rules can be applied across word classes
interactions in dialogue systems can be complex due in part to the many states the system can be in
where the pi are predicates and the ti are terms constructed over constants variables and functions
strategies to project negative face which are called negative politeness or deference politeness are mainly carried out by the use of toning down devices accompanying negatively affecting speech acts such as criticizing giving advice requesting or refusing an offer or request
the inapl roprial eness of reading NUM can be explicated front a logical point of view by the fact that we can deriw a contradiction fl oln that reading of NUM and our conceptual knowledge meaning postulates a
in a rift her restricted ret resentation language it is possible to restrict infere n ing in an emt irically adequate way which ensures decidahility of tile problem all hough a flflly expressive language in used to represent discourse
information in the scope of the intensional verb in 17a whose sister reading is expressed in english by 17b is for example not accessible for lexical disambiguation j NUM NUM a einige arzte versuchten ihre schwestern zu heiraten
our inference based reconstruction of the disambiguation process given in the previous section requires oil the one hand that the meaning of the text is adequately represented in an apt ropriate formal representation language which allows the encoding of conceptum knowledge as well
to disalntfiguate even without a fllll understmming of the discourse rcb lexical disambiguation works even very well in most of those cases where tile discourse is inconsistent or its consistency ix not known and the inconsistency test wouht either fail or ltot necessarily terminate
decidability of mp i.e. the decidability of mp for a given formula results fi oin the fact that mp does not make any absolute existential claim on the entities in the world especially on the jr cardinality
by using incomplete theorem provers it is certainly possible to ensure tractability but incompleteness is always a compromise which can be a cet ted as long as the prover computes the desired inferences completely which is in fact hard to show
in order to be able to explain by inconsistency proofs why the sister reading is excluded for 13c but not for 13a one has to assume an incomplete inference system rcb deg otherwise the system would not work correctly and would of course not necessarily terminate
since an extension of the expressive power of the assertional languages would lead immediately to our original tractability problem we have to give up the implicit assumption that lexical disambiguation presupposes the consistency of the discourse if we do n t want to give up lexical disambiguation at all
bv av kirei na cleai pos environments were defined as one postagged string assumed to be one morpheme and were limited to strings made up only of h ragana characters plus comma and period
we used observations from tile edi lcb corpus which is divided into words and tagged as to pos to calculate tile pos environments and then used a raw corpus no indication of word or morpheme boundaries and no pos tags for calculating the string environments
figure NUM shows the lengths of the dependency links in a center embedding sentence la and a noncenter embedding sentence lb with similar semantics
for example the proposed detinition of structural complexity correctly pre on lea re dora the university of manitoba winnipeg m mitoba tnmla
the difficulty in processing center embedding senten es such as NUM hgs been explained by its requirement on the size of tile stack in a parsb r
structural complexity can be used to choose l he syntactic strnctures with l he lowest structural complexity so that the resulting sentence is easier to understand than other alternatives
all connectionist approaches to our knowledge have suffered from one or more of the following problems one parses contains none or too few linguistic attributes to be used in translation or understanding and or it is not shown how to use their parse formalism in a total nlp system
the author wishes to thank dale gerdemann mark johnson thilo g6tz and the anonymous reviewers for valuable comments and discussion
without subsumption checking this leads to spurious ambiguity both rules produce a magic fact with which a subject np can be built
as ellison points out most constraints can be reformulated to be binary
figure NUM adding word and prefix tapes
this can be corrected by the addition of a single statement to verb
for wordl syn form is present participle so mor form inherits from mor present participle
the paths introduced for the present forms illustrate another use of default definitions
for love given these changes the following extensional statements hold inter
and then redefine wordl and word2 to inherit their verb properties from it
the analysis is now almost the way we would like it to be
n t number of word form tokens
the disambiguation of verbjorms belongs to these middle levels
table NUM number of readings of word fbrms in
table NUM number of readings of word forms in
any number of consecutive sections can be used
here is a simplified example of a sab lexicon
second verbs inflect steminitially and mark the subject object and relative referent by prefixes whereby the actual form of each prefix is governed by the noun class of the noun it refers to
this description is equal to the grammatical rule
the rules of the first section are applied first
operation may have two forms remove and select
section NUM shows how our approach differs from the approach taken by cutting and kupiec
as said earlier the training corpus was manually tagged and contained NUM NUM words
this particular example provides strong evidence of the usefulness of contextual disambiguation with genotypes
instead nmp occurs NUM out of NUM times and becomes the best candidate
in our system we manually tagged about NUM NUM words NUM in this way
adjective feminine singular jfs noun feminine singular nfs noun masculine singular nms
how can the numerous morphological variants that render this task even harder be handled
additionally they are used for smoothing which is a particularly important issue in the context of small training corpus
in this case it is no longer true that jmp is the best candidate
automatic morphological disambiguation is an important component in higher level analysis of natural language text corpora
large in lower parts of the thesaurus since we focus oi1 examples which have a japanese verb v l and a japanese case marker p
we are planning to make experiments on sense classification without bilingual information to evaluate the e lt ectiveness of such bilingual information
the result of the experiment indicated that the t r posed sense classification method has achieved almost pure classification while the result seems a little liner than hand elassitieation
the bilingual class frame association score measures the association of an english class c and a set of pairs of a japanese case marker p and a japanese noun class cs marked by p
we now apply the word class association score to the task of measuring the association of classes of english predicates and japanese case element nouns in the collection of bilingual surface ease structures
this pal er proposed a t ilingual class based method for sense classification of verbal i olysemy which is based on the maximization of the bilingual class frame association score
the classes starting with NUM NUM NUM NUM and NUM are subordinate to abstract relations a qents of human activities
by searching for a large association score it becomes possible to find any combination of case markers which characterizes a sense of the verbal polysemy vs
in the previous section we assume that for any polysemous japanese verb v j there exists a case marker p which is most effective for sense classification of verbal polysemy vj
the objective of this pal er is to brmalize the intuition al out l he comph xity of syntactic structures
on the other hand the length of the dependency link fi om the verb to the adjunct modifier is reduced by the length of the np
figure NUM illustrates how extrapositions atlhct the lengths of dependency links is NUM NUM NUM and NUM
the hypothesis that extraposition must reduce the structural complexity also explains why in heavy np shift the extraposed np must be heavy i.e. consisting of many words
he objecl ive of this pal er is to fortnalize the intuition a bout the complexity of syutactic stru tures
the examples ltresented in the previous sect ions are also consistent w ith a definition l hat uses the inaximum length of structural links
the extraposition of an element is only warranted when the structural coml lexity of the sen i en e is reduced as a result
itowever da ta from psycho linguistic experinlenl s suggest h3t the y a re in fact slightly easier to proce ss
yet the readability of texts has up to now heen measured by tlje lengths of sentences and familiarities of th words in the documents
of course there art still many open issues
there appear to be two main reasons for this
any np may be subject to quantifier raising
we then confront the three theories with the data
b no peter only likes sarah
we explain below how results similar to pustejovsky s type coercion may be obtained with a method based on this domain model instead of a qualia structure
considering two grammatically linked predicates the product of their models constitutes as many model pairs that can be potentially used to look for possible chains
additionally in the target normalised conceptual representation what constitutes the specific theme in our conceptual model the purported oh j of action angioplasty must be precisely defined
provided a new domain and task with the corresponding domain model this enables the generic method to adapt directly to this new domain and give results that are specific to it
for NUM sentences received the link resolution procedure was called on NUM NUM grammatical links and exi lored NUM NUM chains with an average of NUM chains per call and NUM per sentence
the commercial products for internationalization are designed to support the marketing of a tool in a specific set of foreign countries where the menus buttons error messages and text all need to be displayed in the appropriate foreign language
it currently supports paracers fast data finder search engine with support for excalibur s retrievalware currently being developed
an archival database using the fast data finder was implemented using paracers batch search server bss product
for example we located a large japanese to english thesaurus that was available in electronic for l it would be very useful for native english speakers to look up relevant words in the japanese thesaurus for assistance in building their queries
our user community consists of native english speakers who want the menus and buttons to appear in english but require support for viewing foreign language documents in their native scripts as well as entering foreign language query terms in their native scripts
maximize performance spot was designed to be the user interface for a large archival database of hundreds of gigabytes of data
null one of our design objectives was to handle multiple search engines within the same user interface tool
the interface is targeted for the non native speaker and includes a variety of tools to help formulate foreign language queries
consequently subsets of constraints in the above rule can be thought of as possible rules if not complete for the generation of anaphors in chinese
the results suggest that the one we have chosen and which has the most complex rule is better than the other two
each box is divided into three parts which represent the topic the subject and the direct object positions of the clause
after the text planning is finished the decision of anaphoric forms and descriptions is then made by traversing the plan tree
however similar to the situation in text NUM the speakers have varied agreement on the choice of anaphors for the topic shiftings in these two texts
there art NUM anaphors where the kappa score including tr3 is less than that for the speakers alone in many other cases the results being better
by adopting this approach we need not worry about the problems of either of the evaluation methods stated above except the objective evaluation of output text
can not be presented here for reasons of space except that the anaphor generation rules used in the referring expression components are different to each other
upper cases in morphological output indicates one of the non ascii special turkish characters e.g. g denotes u denotes i etc
NUM the fourth and fifth columns in this table give the average parses per token and the initial precision assuming initial recall is NUM
null in turkish there are ambiguities of the sort typically found in languages like english e.g. book noun vs book verb type
our current and future work in this framework involves the learning of constraints and their votes from corpora and combining learned and hand crafted rules
the results after applying this step on of the previous two steps are shown in the last column of table NUM labeled v r c
the following rule with two constraints matches parses with case feature ablative preceding a parse matching a postposition subcategorizing for an ablative nominal form
in this sense this text is our training data while the other three texts were not considered in rule crafting
this paper presents a novel approach to constraint based morphological disambiguation which relieves the rule developer from worrying about conflicting rule ordering requirements
i ll kiss you even if you do n t r rthis example was provided by marc gawron p c who attributed it to carl pollard
the eases illustrated by NUM and NUM 8we construct a representat ion as if the connectives if and even if were simple conjunctions
a strong tendency is indicated to prevent actions the reader is likely to consciously execute using the dont form
in our coding awareness was then shifted to awareness of bad consequences rather than of choices per se
note that the majority of the make sure examples were removed here because they were ensurative
it may for example be used when h might execute an action in an undesirable way
for the form feature the kappa wfiue is NUM NUM which is not surprising given its syntactic nature
until very recently these values would most likely have been accepted as a basis for fllrther analysis
these grammatical forms NUM occurrences in all constitute NUM NUM of the expressions in the full corpus
we felt it acceptable therefore to view the examples as independent and use the x NUM statistic
alternatively an example is coded as unc when a has to be intentionally planned for but the agent may not take into account a crucial feature of a as in NUM do n t charge or store a tool where the temperature is below NUM degrees f or above NUM degrees
it also describes how the levels constrain each other mutually
this argument list is the value o the new arg s fbature
NUM NUM a himselfshaved rcb ohn sui at b
confirms that binding principles can not be defined in terms of linear word order or c command
NUM a a maria falou corn o pedro acerca de si pr6prioi
in NUM a john can not bind himself
a maria falou prep x o pedroi obl
in active constructions it may he bound only by the subject
thus neither the syntactic methods nor the selective approaches can fully satisfy the constraints of robustness and of exhaustivity spoken human machine communication needs
as a resttlt the in epositional cells of the structural layer tnodulate dynamically the case based dispatching weights to prohibit any inconsistent priming
the overall performances of the lfg suggest nevertheless that a syntactic approach is not suitable for spontaneous speech by opposition with the microsemantic one
they involve indeed sevcral lexemes which share the same lnicroselnantic case 1l select the device the right tevice
i draw a circle as soon as the square is erased as a result subordinate clauses are parsed like any ordinary object
it seems however unlikely that it is appropriate for higher level tasks which involve a more complex communication between the user and the computer
it ii NUM spccifi catitm focusing dispatching prmung collection contextual adaptation relational priming l i i igure NUM
we have then described a semantic analyzer based on an associative priming network which aims at parsing spontaneous speech without considering syntax
e.g. in order to derive fact NUM magic fact NUM is unified with the magic literal in the modified version of rule NUM in addition to the facts NUM and NUM
magic rip csem index l magi c s p0 p vform ssem vp p1 p vform csem ssem
even when cycles are removed from the magic part of a compiled grammar and indexing is used to avoid spurious ambiguities as discussed in the previous section subsumption checking can not always be eliminated
for slot based models with tile independence assumption p x ith NUM and
under the independence assuml tion tile nmnber of parameters in a slot based model becomes NUM
to test how large a data size is required to eslimate a class based model we conducted the following experiment
figure NUM a number of dependencies versus data size and b kl distance versus data size
wc used chc cttl irc bracketc l corpus as iil a illillg
when xi takes on NUM or NUM as its value we call the model a slot based model
all three transducers are sequential i.e. deterministic on the input side
a stxingcopy dislhleney is just one that involves a repeat of part of the sentence ul t ered so far NUM
these can either be extracted raw or in context with pronominal references resolved and any logical antecedents included
for the information retrieval task only the clauses containing query document matching terms will be coded for transitivity
c an action is either wholly or partially completed according to whether it is telic or atelic e.g.
news discourse was chosen because it is narrative based and therefore broadly applicable to the notion of transitivity
all terms are treated as equal so that discrimination between documents is based purely on accumulative transitivity scores
certain sections of a narrative are crucially linked with the temporal sequence of events which form the backbone of a text
the total transitivity weight for an entire document is the sum of clause weights normalised by document length
the output dataset consists of a total of NUM news articles an average of NUM NUM per batch
it is therefore inherently linked with a clause containing two participants in which an action is highly effective
it is broadly the notion that an activity is transferred from an agent to a patient
once one has an application in mind then there are three main approaches one can adopt to build the lexicon lexicographic very attractive for nlp applications at first sight as they provide a useful description of the vocabulary entries are distinguished on the basis of multiple senses and we would like to thank in the mikrokosmos team tom herndon jeff longwel oscar cossio and javier ochoa
conclusion in this paper we argued that although lexicographic and statistical approaches have their place in natural language processing computational semantic lexicons are necessary for a wide range of phenomena and are applicable to a number of purposes
this is crucial to let e.g. prepositions influence the choice of the conceptual link and the resolution of the metonymy
it has been fully implemented and used with a reasonable size knowledge base as a part of the menelas text understanding system
besides one can not think of envisioning all the possible uses of such a relation partly because of the use of metonymy
sowa discusses how background knowledge could help to process these metonymies based on a knowledge description of what prix goncourt involves
the methods described have NUM een imi lemente l and applie l on french texts in the medical domain
they exist optionally which makes them present if checking for the left or right context requires them and absent if they are not allowed in this place
the relation inserts instances of all brackets on the lower side everywhere and in any numl er and order
inside the string abe i e laetween a and b and between b and c all x will be ignored any number of times
in order not to confllse the notation by a non standard interpretation of the notion of empty string we introduce a special pair of brackets
the upper string contains zero or more instances of ui possibly interspersed with other material denoted here by x y and z
p c eweoe eo e eo NUM
for example the decision tree for state NUM of the machine in figure NUM is shown in figure NUM
the same NUM binary phonetic features used in calculating edit distance were used to classify phonemes in the decision trees
figure NUM alignment of importance with flapping r deletion and t insertion
an example of the result of this initial tree construction is shown in figure NUM
ostia now attempts to generalize the transducer by merging some of its states together
in the output string indicates the arc s input symbol with no features changed
using information on the alignment between input and output strings allows the algorithm to learn more compact more accurate transducers
however as shown by the literal translations stylistically inadequate sentences would result if this preference were equally applied for english
however it has an effect on grammatical realisation such as erasing the subject during the transition to surface syntactic level
tile english realisation and the first french option 1f rely on a simple correspondence between the predicate fill and corresponding verbs fill and remplir
NUM NUM take advantage of lexicographic descriptive concepts offered by mtt in particular the well known notion of lexicalfunction
optional participants need not necessarily be included in the verbalization if they are present in the sitspec they may be omitted if there is some good reason e.g. a stylistic preference if they are not present in the sitspec the verb can be used anyway
figure NUM shows four which is ilq ut lotlg a t ter the first mouse input ff r example i tnimtt e a fter
strem a ud the mouse l oilll ittg mode 0house stxeam l lcb ules iu the level i sec NUM ion define single tnoda
level NUM a eoinbination of incomifle te mode inputs eomph lnent each other each t lod input does not
about two humlred nmlti ttnoda NUM exln essions front pol ent iaj users a s it st r jctiol s
figure NUM multi modm a pplication written in m i i cg these processes form one cycle in the systent evolution
tilus we had to develop ill ow l de sign niei hodof ogy opi inilze l or nullt i nioda l
fly could choose from the menu aquery i utccion point at the dinosaur and then mouse fly could type t o a conmm ml
this does n t require us l assunlo thai ileol le a lually use conl exl fl ee grammars and colllp lte holllolnort hisills ill order NUM o itnderstand natural languages just thai l he c mlt ul ational model should lm at least approximat ely
the g NUM statistic appears to be more suitable for this type of data since it uses the actual frequency values for the words in the wfls rather than just their ranks
the problem is that the coverage of the n grams is likely to be sparse and any lm so built will be degenerate since it does not reliably predict the characteristics of the source
sufficient y well principled to merit further investigation i NUM NUM language model quality a lm is built by collecting trigram bigram uuigram data from a training corpus
this may be due in part to the particular training corpus used but it is more likely to be inherent to the medium since email can fulfil so many communicative functions
for instance in i igm e NUM ihere is extra leaf in tree i in oml a rison to the tree in a while tree c has a leaf label diffc ence
given the vertex list sequence for a query tree exact match over the trie can be performed using the standard t ech niques by fbllowing the edge labeled with next vertex list until a loft in the trie is reached md the query vertex label sequence is exhausted
closer to the root of the tree are considered to b more serious than differences further away on the root it is os sible to mo lify the formulation to take this into tccotl nt
the algorithm has been implemented on sparcstations and for large randomly generated synthetic tree databases some having tens of thousands of trees it can associatively search or trees with a small error in a matter of tenths of a second to few seconds
we will use x of length rn to denote the query vertex list sequence and y of length n to denote the sequence that is a possibly pattie i candidate vertex list sequence from he database of trees
note that only the forward probability is accumulated NUM is not used in this step
our description of earley parsing omits an optional feature of earley states the lookahead string
in particular the string probability p s g x can be computed as NUM
there inside probabilities for all positions and nonterminals are computed regardless of possible prefixes
NUM to avoid unwieldy y notation we adopt the following convention
this can be accomplished by attaching two probabilistic quantities to each earley state as follows
the significance of earley paths is that they are in a one to one correspondence with left most derivations
where the rule probability p is usually written as p x
the relationship between earley transitions and derivations will be stated more formally in the next section
thus for example ebl pruning was better than ebl pruning on NUM examples and worse on NUM
our interpretation of these results is that the technical loss of grammar coverage due to the specialization and pruning processes is more than counterbalanced by two positive effects
the loss of coverage due to grammar specialization also appears comparable though we have not yet had time to do the work needed to verify this properly
the most immediate consequence is that much larger training corpora can be used before the specialized grammars produced become too large to be handled by the lr table compiler
the lattices were analyzed by four different versions of the parser exploring the different combinations of turning constituent pruning on or off and specialized versus unspecialized grammars
the system implemented in common lisp uses a syntactic filter to eliminate candidate antecedents in impossible
we performed some comparisons of coder responses with one another based on our three success criteria
in the testing of the system we examined each system component separately as described below
NUM we are continuing to experiment with more sophisticated ways of measuring the similarity of parallel elements
however this is because clause rel requires the syntactic filter to make a contribution
the composite factor is a combination of post filter syntactic filter and clause rel
second we began with the complete system and deactivated a single component
based on our empirical research up to this point we concur with this
the system is evaluated by comparing its output to the choices of human coders
here the correct antecedent is the circled vp headed by felt
the set of all positive and negative contexts will not generally determine a unique rule but will determine a set of possible rules
overall the category based approach shows the best performance followed by the cluster based approach k nn shows the worst performance
this paper describes smes an information extraction core system for real world german text processing
furthermore the tdl interface opens up the possibility of integrating deeper processing components very straightforwardly
the majority of existing information systems are applied to english text
a bidirectional lexical driven shallow parser for the combination of extracted fragments
text scanning each file is firstly preprocessed by the text scanner
the shallow parser then applies for each anchor its associated fcp
in that case their default value nil is used as an indication of this fact
4brill reports a NUM accuracy using a training set of NUM NUM words and NUM rules
loves hisl cat the sloppy reading results from a change in context in which the value of NUM becomes john rather than tom
this means we must add an alternative rule for infl so that it adds a property that is the discourse center
gardent makes a proposal similar to the current account a dynamic approach in which paycheck pronouns and vpe are treated uniformly
this framework has for the sake of simplicity restricted l he study of anaphora to pronouns that are extensionally identified with their antecedents
another common approach to lexical rules is to encode them as unary phrase structure rules
besides that many fragments are excluded at certain intermediate points
i am grateful to the anonymous referees for comments on the preliminary version of the paper
in NUM cases syntss were built NUM sentences were evaluated as quasi correct i.e. they had
the corrector employs the morphological and syntactic dictionaries of russian which are part of the processor
in most cases such sentences are semantically and or pragmatically abnormal
the paper describes an application oriented system that corrects agreement errors
thus the corrector s reaction was right for NUM sentences
a standard entry describes syntactic properties typical of the words with a particular type of paradigm
being a language of the inflectional type russian has a rich system of word changing
in fact one character words represent slightly less than NUM of all lexical entries while two character words take up more than NUM
NUM segmentation unitde f is the smallest string of character s that has both an independent meaning and a fixed grammatical category
first nontechnical terms are deliberately chosen such that even developers in information industries with little or no linguistic background could follow this standard
the fact that chinese writing does not mark word boundaries poses the unique question of word segmentation in chinese computational linguistics e.g.
the tripartite segmentation criteria consist of a definition of the segmentation unit two segmentation principles and a set of heuristic guidelines
NUM NUM components of the sezmentation standard our proposed segmentation standard consists of two major components to meet the goals discussed above
data uniformity is achieved by stratification of the standard itself and by defining a standard lexicon as part of the segmentation standard
besides even when they are available they are not systematically used as in the english versions as attested by the following example 12e pvessurise the hydraulic system
for aeronautic maintenance procedures controlled languages in particular aecma aia simplified english and gifas rationalised french provide useful guidances which help to identify the relevant differences for multilingual generation
in general the two types of constructions represented by rules rl nd r2 are possible even when some attribute of the action should be realized at surface level
the design of a multilingual generation system needless to say requires a precise analysis of the linguistic means used by each language to express the same conceptual content
and the argument can be any words
intuitively u d according to a prelimhl y
for example figure NUM shows a f a rt of isamap
in figure NUM areal is pro ferre l to area2
i nodes are represented i y hlack circh s
NUM a sorede tyotto kangaesasete so a little think cause pass so i said let me think for a while NUM b hosii tte itta no
this concept node is also triggered by the verb murdered bat is activated only when the verb appears in an active construction
some manual intervention is still required in this case but autoslog ts significantly reduces the amount of effort required to create an appropriate training corpus
we describe a system called autoslog ts which is a variation of our previous autoslog system that runs exhaustively on an untagged text corpus
second we describe the original autoslog system for automated dictionary construction and explain how autoslog was adapted to generate patterns from an untagged corpus
we were also interested in gathering data to suggest how the autoslog ts dictionary could be filtered automatically to produce an effective dictionary for information extraction
for example n NUM means that we retained a concept node if NUM of its occurrences were in relevant texts
because autoslog ts is not constrained to consider only the annotated portions of the corpus it found many good patterns that autoslog did not
we believe that spoken language translation systems need to be able to translate what is conveyed through natural speech properties in order to fully convey the speaker s intentions in verbal communication
also a number of automatic type definitions are added according to the nature of the data integers labels
the bnc lm with the email vocabulary performs better by NUM NUM correct than the bnc lm with the bnc vocabulary so clearly the email vocabulary provides better coverage of the test data
but these data are rather constrained in such a way that it seems that tile discourse referent at stake is an abstract one nmnely a fact and not an event state
to help illusl rate this we shall use the following exam ph where wc assume that lb is the scntential negro ion of la
null NUM cases in the corpus seem however to suggest that negated event sentences may denote events NUM a elle ne le voulut pas
we want to show through linguistic a rgmnents that the best semantic rcl resentation li least for sentential ncga tion
briefly our approach is motivated by the shortcomings that we perceive in other approaches such as syntactic or semantic grammar based interlingua based purely analogical or purely statistical methods
we report results of lexical accommodation studies involving three different interpretation settings human human monolingual human interpreted bilingual and machine interpreted bilingual
in order to explore this question we estimated the percent of usage for each worddncommon for both client and agent
alternatively preferences based on accommodation could be built directly into a language model for speech recognition or decision tree for parsing
on the other hand the human interpreted setting presented a more difficult communication environment in which concern for communicational efficiency was present
clients in the machine interpreted setting may have perceived the machine to be in the dominant role just as the agent played the dominant role in the human human setting
considering the high accommodation rates for this setting figure NUM we conclude that in fact both client and agent were accommodating to one another
in addition the lexical accommodation rates for each experiment also differed significantly from those for each of the other two experiments
that level was achieved as a result of mutual accommodation between the two humans involved both of whom felt a concern for both social standing and communicational efficiency
this is consistent with the interpretation that the agent acted as informationprovider and the client acted as information receiver in a non stressful communication environment as our initial hypothesis stated
but while the phenomenon of accommodation both between humans and between human and computer is amply demonstrated the motivations behind the phenomenon are less often discussed
t hen exj tloop tree inark d sambigui tted
the perlbrmance of the procedure was tested on four semcor texts chosen at random
conceptual density initial mutual constraint size is NUM and window size is NUM
conceptual density has been used for other tasks apart from the disambiguation of free running test
german rigau was supported by a grant from the ministerio de educaci6n y ciencia
tile measure should be independent of the lltllllber o1 concepts we are measuring
in speech translation in the context of spoken language translation the crucial characteristic of pragmatic utterance strategies is that the surface forms in which they are realized are often different across languages
for example the same conversational participants may use different linguistic forms depending on the speech situation such as a discussion during a formal meeting versus an informal hallway chat after the meeting
with tile description length of a model defined in the above manner we wish to select a model having the minimum description length and output it as the result of clustering
i efine a probabilistic model consisting of the l artition of nouns si ecified by the two sul sets and th entire set of verbs
for example million and billion are classilied in one iioll i chlster alld stock and share arc classified together
we refer to a member of the cartesian product of a noun partition and a verb partition c p v x pv simply as a cluster
we coml ared the performance of elnploying m1 as a criterion in our silnulatcd annealing algorithm against that of employing m ia by simulation experiments
for the NUM words that appear in the position of oun e in the test data we constructed a thesaurus based on the co occurrences between heads and slot
we often refer to lm od l q lpar l as the model description length
i know her age and her address
the paper is organized as follows
using these measures sense clusters are discovered in the
in a the expected focus is baseball the theme in b it refers to baseball cf
the algorithm is based on the decomposition of the sentence into ees and the application of the basic focusing cycle on each ee in turn and not sentence by sentence
the anaphora interpreter uses the state of the focus and a set of algorithms associated with each anaphor type to determine which element of the data structures is the antecedent
the main cases of failure were due to the cases that were not considered by the algorithm like for example the pronouns occurring before their antecedents i.e. cataphors
the problem with initial anaphors is that the focus registers can not be initialised or may be wrongly filled if there are anaphors inside the first sentence of the text
assumption NUM redefines these relations in an informal and less rigorous way at the semantic level i.e. considering semantic parameters such as the type of verbs that introduce embedded sentences
the focusing algorithm decides that since no anaphor refers to the cf the cf is stacked and ice cream cones is the new cf focus movement
for example the verb to say that takes a sentence complement favors an introduction of a new fact i.e. to say something and the related fact
the statistics below show that of NUM non prr pronouns there are NUM having intrasentential antecedents all of which occur in embedded sentences and none in a simple sentence
the only user initiative our system permits is that users may initiate clarification and repair meta communication through uttering the keywords repeat and change
this distinction between the needs of expert and novice users was introduced in woz iteration NUM when several users had complained that the system talked too much
in such dialogues lie claims adherence to the maxims is rational because it enstu es ihal the interlocutors pursue the shared goal most efl iciently
we systematically analyzed the transcribed dialogues to detect those deviations between expected and actual user utterances that would signal problems of user system interaction caused by non cooperative dialogue design
it canuoi be allowed for instance that the system confirrns infornlatioll which has ilol been checked with the database and which might be false or impossible
one result of analyzing ihe rehltionship between principles and nlaxhns is the distinction shown in the tables NUM elween ps eneric and specific principles
it is exactly when a hunian or for that inatler an slds non deliberately viohttes a maxim that dialogue clarification problems are likely to occur
among several research topics in this field acquisition from parallel corpora is quite attractive e.g.
the first stage of processing is a scan of the text to establish its format and for large files to delimit a sample to be annotated
moreover the ernail co us really stands out on its own having a very poor correlation with the others in many eases it is negative
this is encoded as an fst that writes marks on an output harmony marks tape
align prefix states that the prefix should remain as close to the front of the word as possible
z in this work we lay the theoretical groundwork for using ot as a parsing tool
using the harmonic marks to prune the resulting transducer reveals the optimal candidate figure NUM
ellison s approach gives us an elegant method of performing ot generation using finite state automata
for invertability it is critical that the fst have access to both input and output forms
the remaining question however is whether faithfulness constraints can be modeled by regular grammars
taking the product of this language with the optimal candidate scores the candidate figure NUM
using these constraints the ot fsts should be able to generate and parse in the tagalog example
any ws before ps on the morpheme structure tape get a harmony violation mark
finally call nl the root of qp and n2 the root of q example NUM figure NUM depicts trees q and qp
a parse tree is then kept iff the lig conditions are valid on that tree
as an example consider the denotation of the causative reading of to fill
summary tile extensions introduced now can apply in a sequential order to a verb
the dxt contains the denotation subgraph that the new verbalization has in addition to the old one
this is the most basic one denoting an activity the water drained from the tank
only when building the sentence semspec is it relevant to know that the connectee can be omitted
we think the term is overloaded and prefer to use resultative for the latter group
cut mi ation culmination figure NUM situation types in the ontology of moose
locative extensions example a sally sprayed the wall with paint b
the upper model in its present form can not make distinctions of this kind
to disconnect requires a source but it can be omitted in a suitable specific context
because it uses only the mlrftu information in sent races and does not n cd a huge amount of common sense knowledge to rcsolvo zero pronouns it is easy to implem nt i on computer syst ms
how woi stlch s is ii w difficult becaus the t i cvious mt caing theory has only handled the t tw itotltodtgl ilt sll cc qsivo
we obtained NUM correct only adding a constraint stating that the sense chosen for a word must be compatible with its pos tag
tained using ternary constraints in combination with other kinds of information are shown in rows t bt tc and btc in table NUM
here the emphasis was on the utility of speech i o in an eyes and hands busy virtual environment
of the remaining NUM utterances nautilus failed to understand NUM NUM due to incomplete coverage
the robots currently recognize two types of gesture distance hands held apart and direction wave left right
the focal focus algorithm reference resolution module was developed by visiting mit graduate student gina anne levow
it resolves definite indefinite and pronominal references as subsets of objects from a closed world model developed for each application
to date the system accepts commands only using a NUM word speech vocabulary with an input range of about NUM NUM utterances
similarly for speech output we provided the same vocabulary to dectalk along with phonetic transcriptions to produce acceptable german pronunciations
focal uses semantic class number recency and constituent order within the sentence when choosing antecedents for anaphoric references
funtran also composes simple fragmentary english responses to database queries based on the results of the tf predicate evaluation
each restriction rule is attached to a particular nonterminal in the right hand side of one or more context free rules
this paper focuses on local cohesion
on th ret orent of the pronoun it
supplementatiml of elliptical phrases is another typical context dependent prol lem
a contains b c is also included in a
the procedure is roughly as follows NUM once it is established that no appropriate entry is in the lexicon NUM arbitrarily select a category for an open lexical class NUM check whether it fits the given context and NUM if it fits take the final feature structure for the form which is instantiated in the course of a successfully completed parse
the intuitive idea is that we consider categories to be left recursive if their tokens can be unified rather than being identical as in the case of atoms we then use their generalization or greatest lower bound as a common denominator defining an equivalence relation
we must take care not to exclude a mother s children sleep which will happen if the linking relation is defined so that any determiner also in a possessive construction must have the same agreement features as the sentence subject of which it is a left corner
the research project dynamische erweiterung des lexikons dynalex is supported by the german research foundation dfg in the sonderfbrschungsbereich NUM lheorie des lexikons NUM wish to thank petra barg christof rumpf sebastian varges and anonymous referees for their comments and suggestions
NUM NUM feature space for the docmnent classification
our approach is the following NUM
words that are indicative of the sense usually appear in the training set more than what would have been expected from their frequency in the general corpus
as the mrd we used a combination of the webster the oxford and the wordnet online dictionaries the latter used as a thesaurus only
results on two of the words on which we tested our algorithm drug and suit have been also reported in the works of schutze and yarowsky
the final algorithm was tested on a total of NUM examples of four polysemous words drug sentence suit and player see table NUM
our measure of similarity is transitive allowing two words to be considered similar even if they are neither observed in the same sentence nor share neighbor words
l liis process tl ad l olisidcral lc iiilll il iers NUM lags o i rcb l ahoy tot als
lags al lures the lifft r c t etwc m e.g.
for instance for the cv o a f hys iologist dewey NUM and NUM medical sciences lumen physiology diseases were chosen
a treebank is a body of natural language text which has been grammatically annotated by hand in terms of some previously established scheme of grammatical analysis
the atr lancaster treebank consists of approximately NUM NUM words of grammatically analyzed text divided into roughly NUM documents ranging in length ffmn about NUM to about NUM words
each document is classifed according to tone style linguistic level point of view physical description of document geographical background of author etc
we assume here that irregular verbs are encoded separately
in these results the generative model performs significantly better than the others and does about equally well at assigning pa rtof speech tags
has one pa rcnt lcss cndwor i its sul sl tn b lists two
we need separate brackets for empty and non empty upper
a the tagged sentence b a candidate
table NUM results on section NUM of the wsj treebank
more sophisticated estimation techniques such as deleted interpolation should be tried
tags can be ignored when lexical information is available by defining
in training data NUM of commas follow this rule
pos tags NUM the two pos tags alone
second the parser is a method for finding tbest
a statistical approach to this problem consists of two components
figure NUM each constituent with n children in this
where NUM is the set of all triples of non terminals
l d le NUM lcb csults of preliininary xl crimcnts per
just like top down evaluation of the original grammar bottom up evaluation of its magic compiled version falls prey to non termination in the face of head recursion
after more detailed empirical investigation this will be refined so that the possibility of integration weighs in favor of the multimodal interpretation but it can still be beaten by a unimodal gestural interpretation with a significantly higher probability
for the application tasks described here we have observed a reduction in the length and complexity of spoken input compared to the unimodal spoken interface to leathernet informally reconfirming the empirical results of oviatt et al NUM
we believe that the unification based method described here will readily scale to larger tasks and is sufficiently general to support a wide variety of other application areas including graphically based information systems and editing of textual and graphical content
object echelon platoon unit create unit location point since quickset is a task based system directed toward setting up a scenario for simulation this phrase is interpreted as a partially specified unit creation command
since the speech interpretation figure NUM requires its location feature to be of type point only unification with the point interpretation of the gesture will succeed and be passed on as a valid multimodal interpretation figure NUM
therefore in our multimodal architecture the integrator temporally licenses integration of speech and gesture if their time intervals overlap or if the onset of the speech signal is within a brief time window following the end of gesture
if speech is temporally compatible with gesture in this respect then the integrator takes the sets of interpretations for both speech and gesture and for each pairing in the product set attempts to unify the two feature structures
complete commands can originate in a single mode yielding unimodal spoken and gestural commands or in a combination of modes yielding multimodal commands in which speech and gesture are able to contribute either the predicate or the arguments of the command
the user s ink is likely to be assigned a number of interpretations for example both a point interpretation and a line interpretation which the gesture recognition agent assigns typed feature structures see figures NUM and NUM
now we are able to define the expression utterance in a satisfactory manner an utterance u is a simple sentence a complex sentence or each full clause of a compound sentence NUM the c of an utterance is computed only with respect to the matrix clause
we follow here the latter approach
although the method is efficient for large corpora it involves large volume of fractional and unnecessary expressions
the n gram frequency table is constructed by counting the number of pointers which represent the same prefix parts
NUM for any pair of chunks in an english sentence repeat the operations done in the the japanese sentence
this architecture reflects the fact that fixed collocations play a more crucial role than accepted in previous research
another drawback of their approa ch is that only the longesl n gram is adopl ed
more than NUM of the strings extracted by nagao s method are removed with the new method
the points when extracting flexible collocations is how the number of combination candidates can be reduced
a number of studies haw aj tetnpte i to extract bilinguaj collocations from paralm corpora
as well as no NUM a nuuflml ofseut ence level collocations were also extracl ed
because our method automatically extracts these collocations it will be of significant use in compiling domain specific dictionaries
the problem arises due to inappropriate use of the additional formula z which should only be used to prove the argument y just as z s role wouhl be to contribute to proving the argument yo z in a standard proof involving the original formula xo zo yo z
the mel ho involves orepiling possibly higher or l r linear formulae NUM o indexed firsl or ler formulae over whi h tcdu l ion is ina le using jusl a sin
differing in their resource sensitivity and hence imt licil ly heir underlying llo ion of linguisli sl rllei llre in some eases combining differing resource sensicivil ies wi hin a single sysl e n
consider an attempt to prove the invalid type combination xo zo yo z y x compilation of the tbrmula xo zo yo z yiehls two formulae xo zo y and z so tile initial query becomes xo zo y z y x which is provable
this t al er present s a method applicalle to parsing a range of cal egorial gramm u formalisms in pa rl ieulm ones lhal l tll wil hin l he yt e logieal t ra dition of which lhe associa l ive
these requirements carl be enforced using an indexation method whereby each initial forinula in our dat at ase is marked with a unique index or strictly a single era set containing that index and where a formula that results ti om a combination is inarked with the union of the index sets of the two formulae combined
we present a translation algorithm for tts that can immediately be converted into a transformation based parsing algorithm
let n be the set of all nodes from the trees in lhs r
in this section we relate our work with the existing literature and further discuss our result
thus it needed to be replaced by x2morf
the morphological component of fuf is very restricted
rcb oincers are used co enforce scl llcl tlre sharing and provide a neans lcb percolace informacion within a eacm e sl rllecllre
adjectives provide a good case study for that
therefore our system does not grossly overgenerate candidates
most lrs in the generative lexicon approach however have been proposed for small classes of words and explain such grammatical and semantic shifts as count to count or common to common
word formation the production of derived forms by lr is illustrated in a case study below and includes formation of deverbal nominals destruction running agentive nouns catcher
the following phenomena seem to be appropriate for treatment with lrs inflected forms specifically those inflectional phenomena which accompany changes in subcategorization frame passivization dative alternation etc
in other words they typically modify the noun which is the theme or beneficiary if animate of the verb from which the adjective is derived one can read the book the
lrs are viewed as a means to minimize the need for costly lexicographic heuristics to reduce the number of lexicon entry types and generally to make the acquisition process faster and cheaper
acquisition time the major advantage of this strategy is that the results of any lr expansion can be checked by the lexicon acquirer though at the cost of substantial additional time
a complete parse figure NUM is any binary parse of the wl wi s sequence with the restriction that s is the only allowed headword
the operations performed by the parser ensure that all possible binary branching parses with all possible headword assignments for the w wk word sequence can be generated
the following algorithm describes how the model generates a word sequence with a complete parse see figures NUM NUM for notation
the model consists of two modules a next word predictor which makes use of syntactic structure as developed by a parser
note that wl wi need n t be a constituent but for the parses where it is there is no restriction on which of its words is the headword
it is easy to see that any given word sequence with a possible parse and headword annotation is generated by a unique sequence of model actions
to stress this point a word parse k prefix contains only those binary trees whose span is completely included in the word kprefix excluding wo s
to ensure a proper probabilistic model we have to make sure that NUM and NUM are well defined conditional probabilities and that the model halts with probability one
a NUM gram approach would predict barked from heard yesterday whereas it is clear that the predictor should use the word dog which is outside the reach of even NUM grams
tracted verb co occurring with a randomly selected noun is v
if both are zero the analysis is made as if the ratio were exactly unity
in the three word window training scheme the guess rates are less than NUM
in no case do any of the windowed training schemes outperform the pattern scheme
there an additional weighting factor of NUM NUM is used to favor a left branching analysis
it seems that additional instances admitted by the windowed schemes are too noisy to make an improvement
table NUM test set distribution two types of training scheme are explored in this
here ambig w is the number of categories in which w appears
the power of words in message planning
the difference between the time for ne and co is roughly th e time taken by the coreference recognition algorithm
this is largely due to the unified abductive view o f different tasks in natural language understanding NUM
we developed and tested a domain independent algorithm for coreference recognition that turned ou t to work quite well
c john smith who has been xyz s president and ceo since NUM
after each sentence is processed the potential coreference relationships are shown in table NUM
it takes from NUM to NUM seconds to process an article on an inexpensive pc
the lexical items may then be combined or deleted by lexical rules which are responsible for recognizing named entities
other noun phrases r expressions can not refer to a c commanding noun phrase in the sam e sentence
if the weight is positive the pair np1 np2 is a potential instance of coreference relationship
this was not totally unexpected since the implementation of the s t module did not begin until september 29th
at this time we make no claim as to whether NUM is an optimal fornmla for cah ulating evidence weights
more complex validation may involve other words in the phrase e.g. circuit breaker or words in the immediate context
when the combined weight exceeds a threshold value the candidate is accepted and the i hrase becomes available for tagging by the spotter
we believe that tile universal spotter can replace much of the need to create hand crafted concept spotters commonly used in text extraction operations
this c mte gory inay be hard if not impossible to describe by any finit set of rules
we should note here that it is acceptable for homonym entities to have different classification depending upon the context in which they are used
the seed can be obtained either by hand tagging some text or using a naive spotter that has high precision but presumably low recall
otherwise the andidate is reje te l although it may be reevaluated in a fllture iteration
heim sch roder NUM co skandi naviska enskilda ha nken statistics canada
x1 NUM starting with state kx u end in the final state
thus all participants of process types as listed above are coded in loom as obligatory roles
here the case frame of the verb has to encode that from the tank is an optional constituent
to this last form a causative extension can apply and yield tom drained the tank of the water
add the c0v list which has been instantiated by the matching to the covering list of vo
then a language specific semantic specification semspec is constructed in accordance with generation parameters pertaining to brevity and stylistic tbatures
we wish to distinguish cases like the following tom disconnected the wire lcb from the plug rcb
to put requires a destination and it can not be omitted no matter how specific the context
the situation would not be well formed with either of them absent and the domain model encodes this restriction
about to disconnect in the causative reading which is a material process tile um can only state that the roles actor and actee must be filled but not the fact that there is another entity involved in the domain model called the connzctez which is verbalized as a source
the psemspec is modified as follows the former dest inat ion wall becomes the new actee whereas the former ac ee paint now fills the role inclusive and is optional there because jill stuffed the cushion is also well formed
both classes parameters converged after six iterations
if the system adheres to gpi and gp9 there is a maximum likelihood that users obtain the task domain knowledge they need from the syslem when they need it
hlfortn the dialogue parttlors of inll ot tant iloll il ll lllal charactcrislics which they should take into accotilll in order tt behave cooperatively in dialogue
in other words the maxims of truth and evidence are so important to the design o1 sli ss that they are unlikely to emerge duriug dialogue design problenl solving
in the last four years we have designed and implemented the dialogue component of a spoken language dialogue system slds prototype in the domain of flight ticket reservation
moreover in order to adhere to gpi i it is necessary lkn the speaker to recognize relevant differences among inlerlocutors and interlocutor groups in icrms o1 background knowledge
gpi l can not be reduced to gpi informativeness because first gp1 does not refer to the notions of background knowledge and differences in background knowledge among interlocutors
in addition to principles stating how a speaker should hehavc principles are needed ticcording to which the speaker should consider transferring part of the responsibility for cooperation to the interlocutor
comparison between our principles and rice s maxims has shown that there are more generic principles of cooperativity it human machine dialogue than those identified by grice
if a deviation from the graph occurred during the matching process this would indicate a potential dia null logue design problem which should be removed il possible
however or dialogue design ptirpo sos lhe illaxill s illtlst be augnlenled hy task sljecl tc or domain sl ecific princohes such as the bllowing
if the senses were collapsed at the file level the coverage and precision of tile algorithm at the file level might be even better
we will now describe how the description length for a model is calculated lh call that each model is specified by the cartesian product of a partition of nouns and a partition of verbs and a number of parameters for them
since some words never occur in a corpus and thus can not be reliably classified by a method solely based on corpus data we propose to combine the use of an automatically constructed thesaurus and a hand made thesaurus in disambiguation
also mi i tends to select a mo h l overfittil g the data while ml l t cnds to seh ct a model which is simple and yet tits the data reasonably well
where f n v denotes the observed frequency of the noun verb pair n v and p n v the estimated probability of n v which is calculated as follows
the number of noun clusters in the true model is NUM figure NUM b plots the ki distance versus the data size also averaged over l he san NUM trials
a world is a structured set of objects which is coherent the exceptions change of meanings are taken into account by a change of world
in this paper we have presented an object based knowledge representation model that allows to extract and to represent knowledge in the knowledge base from discourse
null our use of the linking relation with destructive unification in parsing requires special comment
the extension of a world contains objects which are particular to a specific situation a specific time peter s dog barked all night long
the types are also linked by their stru tura and fun tire sub ol jects
thus the knowledge associated to a discourse is represented at two levels a knowledge representation of the npplication do main which is outside lira llaturai languag
ni g other names i structural sub objects represent the part o ingredience part all in the sens of the mereelegy
of course c may itself be the category c sought hence the reflexive closure
percentages and means are computed and the sets are compared statistically using the standard t test
the distribution of information over three lexicons has a sinrple reason namely avoiding lexical anrbiguities at places where they can not be resolved or where they have inrpact on eificiency
the paper reports on a text handling component two level morphology word structure phrase structure semantics and and very importan lcb ly the interaction of these components
the definition of such generic entries in the lexicon allows to keep the lexicon smaller but also to deal with a potentially infinite number of words
in addition mpro provides a built in facility for resolving categorial ambiguities on the basis of homograph reductions and a facility for handling unknown words which are written on a file
the was a corpus investigation in the in the beginning in the course of which tools have been used and developed which allow for automatic and semi automatic determination of linguistic phenomena
mmnstream approach none the less the approach we adopted clainls to be mainstream very much indebted to hpsg thus based on the currently most prominent and recent linguistic theory
the values for phenol is a boolean conjunction from two sets sl lcb none no yes rcb and s2 lcb e i
the treatment consists of mapping a surface e to a lexical e in case the constraint which is expressed as a feature structure in the fourth argument holds
comment in the weeks before x mas the proud head of the deutsche aerospace ag which belongs to daimler benz could announce an annual statement of accounts which exceeds all expectations
head selections alep allows for user defined parsing head declarations as the most appro3it should have been shown in the precious sections that felicitous descriptions are possible anyway
our sysl em was deveh ped in c t using libraries for dealing with texts marke l ut in sgml format
for our experiment s the amount of new ambiguities thereby introduced did not cause specific problems for tim system
as we can see these results are in the same range as the results for the machine system discussed previously NUM NUM NUM NUM for abstracts with NUM sentences
it turns out however that these weights do not lead to an improvement of the syst em s performance
we have employed generalization in the definition of linking to make a kind of mask allowing just the appropriate information to become shared between b and b
in implementation missing lexical entries can be dealt with in a first approximation by extending the interpreter with a second clause in the definition of leaf
compared l o about NUM set ends for the machine system the hmnans nee h d rthis provides a bias towards longer sentences
but if b and b are actually unified with x and x respectively information may become shared between b and b
returning to shieber s rules for subcategorization the definition of l given here allows instantiated head features of a vp goal to be passed top down to a verb
the reader is ref xred t o for there information on this algorithm various odter nte thotls that were tested and their respective result s
but we have seen that linking as defined above in terms of feature structures and used in parsing with destructive unification leads to the top down instantiation of information
the main reason that names remain untyped is insufficent evidence in the document
categorizing and standardizing proper nouns for efficient information retrieval in b boguraev and
thus a sentence beginning with yesterday columbia yields columbia as a name
otherwise we choose the highest score obtained by the various heuristics
yorktown heights ny NUM yorktown heights ny NUM yaelcwat son
finally nominator links together variants that refer to the same entity
if a name database exists nominator can take advantage of it
this design choice differentiates our approach from that of several similar projects
it causes a split because it is immediately preceded by an all capitalized word
we have found this simple typographical heuristic to be powerful and surprisingly accurate
the text was first au tomatically aligned and then hand checked by a hum m supervisor
we use the contingency matrix to evaluate the similarity of word chunk occurrences in both languages
sorting by sentno makes it much easier to check the subsumption of word chunks
and ilirakawa NUM lirst extracted japanese n ps in the s iii
their correspotldence prol abilities arc then gradually relined by using an em like iteration algorithm
most of the fixed uninterrupted collocations are directly extracted from the word chunks
next to extract common substrings the pointer table is sorted in alphabetic order
parts of japanese sentence were often omitted and sometimes appeared in a different english sentence
no NUM to no NUM are typical exprossions in stock nlarkc l reports
these exi rcssioiis a pllear eweryda y in st ock markel reports
the results after applying this step on top of voting with m NUM are shown in the fourth column of table NUM labeled v r
our experience is that we can in principle add highly specialized rules by covering a larger text base to improve our recall and precision for the m NUM
l i.e. the parse was wrong if even one tag was wrong or of couree ira rule choice was wrong
moreover it scarcely depends on the language in use becmlsc the way of disambiguation is based oil the inference with a certain knowledge base
given a specific conflict pair of partially parsed sentences the supervisor would add a new relevant feature that discriminates the two examples
in the case that the conjunctive shows causality the matrix chmse should show some inevitable result of tile event expressed by the subordinate clause
for the corpus examined NUM of the fixed collocations and NUM of the flexible collocations output by the method were correct
most cases are actual instances of the past tense
a method for abstracting newspaper articles by using surface clues
magerman s spatter and collins bld results for spatter and bld are for sentences of up to NUM words
if anything admitting additional training data based on the tagger introduces more noise reducing the accuracy
while these changes are motivated by the dependency model i have also applied them to the adjacency model for comparison
by the assumption that words within a group behave similarly this is constant given the two categories
yet when they occur as nouns they still provide useful training information that the current system ignores
a marked improvement is observed for the adjacency model while the dependency model is only slightly improved
for the adjacency model when the given compound is wlw2w3 we can estimate this ratio as
first the use of conceptual association not only enables a broad coverage but also improves the accuracy
accuracy figures in all the results reported below were computed using only those NUM compounds which received a parse
within any single hierarchy the features are ordered according to their difficulty of acquisition reflecting their relative linguistic complexity
having this dual model in the system helps us understand the user s input in terms of the errors that they make
it is also useful in the response phase of the system to focus tutoring on the errors that are most beneficial to correct
that is two languages may express a concept in radically different ways and thus the mapping between the languages may be unclear
these include choosing which errors to respond to selecting an overall type of response and generating the actual english response itself
in addition the linguistic model can be used to provide corrective feedback that is most likely to be beneficial to the user
in this paper we focus on just two aspects of response generation that rely on our model of the learner s generation process
n ines location names dates etc rcb lit those were usually either hand crafted for particular applications or domains or were heavily relying on apriori lexical clues such as keywords e.g. co case e.g. john k big predicatable format e.g. NUM maple street or a combination of thereof
second unlike the word sense disambigumtion where the it eros NUM o be clmssitied arc known apriori we attempt to acconqflish two things at the smnm time NUM discover l he items lo be onsidcred for c mtegorization null NUM acl ually decide if an item NUM elongs to a given category or falls outside of il
subsequently seeds were construct ed ma nually in forln of contextual rule s l i r orgmfizati lcb rcb ns these nit a NUM rules hall a NUM i e lcb ision and NUM lcb rcb recall for products the corresl rcb onding numbers were NUM and NUM rcb
two options x y xy ifx oandy o x o y x y xy ifx oandy o NUM x y otherwise and dy x ifabs x abs y y otherwise NUM k in NUM x i y is greater than either x or y when
the smnmnl i categorization problem described here displmys some pmrmllcls to the word sense dis ambigumdon problem where hoinonylll words ileed to be mssigned to one of several possible senses yarowsky NUM NUM gale chm ch yarowsky lt NUM brown pictra pietra mercer NUM
those constructions that are at or slightly above the user s level will be preferred by ordering them before other potential realizations
it describes the almost unconscious manner in which native speakers of a language automatically simplify their speech to accommodate second language learners
thus in the example above the rule p could be used to weaken the argument from npasga3 to rip but it would not allow np without agreement features to be strengthened to say npa sga NUM
variables range over feature structures and underspecified feature structures
without this constraint the system incorrectly selects the vp advises
furthermore the fact corresponding to the vp buys mary a book john is not included
NUM magic vp vform ssem magic s vform ssem
unfolding can be used to reduce the number of magic facts that are produced during processing
it is possible though to trim the magic predicates by applying an abstraction function
NUM magic vp vform ssem magic vp vform ssem
figure NUM gives a schematic example of a grammar that does not obey the dependency constraint
magic compilation is illustrated on the basis of the simple logic grammar extract in figure NUM
figure NUM abstract example grammar not obeying the dependency constraint
the original rules of the program are extended such that these bindings can be made effective
this tolerant approach is motivated by the frequent ordering violations spontaneous speech involves
rainbow NUM established that any ordering rule should be expressed lexically
the rule NUM stands therefore for NUM
ml4 j NUM t cdegmin otherwise
in such cases the coordinated elements must share the same microsemantic case
ail t amax if i is to the current word
the suitability of the semantic parser is rcally patent when considering spontaneous speech
despite this weakness the robustness of the microsemantic parser is already substantial
coordination coordinate lexemes must fulfil the same subcategorized argument
fl ddpla ons nous le carrd
fhis is indeed the apl roach we take in this lmper
such distributions are referred to as dendroid distributions in tile literature
here NUM indicates the absence of the case slot in question
we forcibly attached prep nou t2 to verb for these NUM examples
furthermore we found that there were NUM verbs having inter dependent preposition slots
we defined two other models and conducted the same experiments
action enabling an action enabling relationship can be captured by a new procedure that given two adjacent inactive edges el and e2 such that el s effects satisfy some of eu s preconditions treaties a new inactive edge with action c2 as its action value
a plan l arsing method has been prol osed that handles the effe ts and l reeonditions of actions and that parses i lans hi a manner del endent m tialogue state changes ause t by utterances
since an action is modeled to have a certain tenq oral extent an action s effect is inodeled to hohl at the point in time where the action has just finished and to continue to persist infinitely or until the first instance that a contradictory fact holds
a time map with a set of fact tokens supports queries about whether it guarantees that a fact holds over an interval t t2 written as tm holds h t2
the set of effects of the action represented by an inactive edge ei that hold at the action s ending time is NUM lcb qo i tm holds lcb qo end ei cnd ei effects i rcb
for example many of the irrelevant texts in the muc NUM corpus describe military actions so the resulting autoslog ts dictionary is especially well suited for discriminating texts describing military incidents from those describing terrorist incidents
figure NUM comparison of dictionaries using relevancy filtering
figure NUM comparison of dictionaries using frequency filtering
NUM NUM autoslog automated dictionary construction using text annotations
although it may still be necessary for a human to review the resulting patterns to build an information extraction system this approach eliminates the need for text annotations and relies only on preclassified texts
finally we filtered the autoslog ts dictionary using both relevancy and frequency filtering n NUM to get a rough idea of how many concept node definitions will be useful for information extraction
murder passive victim three peasants a similar concept node called murder active recognizes active forms of the verb murdered such as terrorists murdered three peasants
however the annotation process is not trivial
16the connected words represent phrases in circus lexicon
this was done recursively therefore if removing an empty bracket pair caused its parent to become empty the parent is also removed
things would be easy if we could assmne that the chance of apl lying a bracket is correctly modeled as a binomial experiment
these algorithms are sensitive to whether the information is old or new in the discourse model incrementally constructed from the translated text whether they refer to salient entities using centering theory and whether they can be contrasted with other entities in the discourse model
in practice m1 and m2 have to be discrete values so they often are not satisfying the condition exactly but are close enough
another NUM roblem is that we only get a very general picture whereas it would be interesting to know much more details
this already gives more detailed information but we can take things a step further by having a human evaluate the most important brackets
in this paper we suggest giving more specific information about test results and develop methods to estimate the statistical significance for test scores
for example how many of the bracket pairs that constituted a crossing error when compared to the treebank would be acceptable to a human
there are two major types of language generation l
discuss my a long agreed on paper with me
we now encounter two simultaneous stems
this is not possible however in a spoken language translation system that acts as a human human verbal communication aid where the expressive information encoded in utterances plays a far bigger role
probabilistic methods of implementing these principles have the merit of being able to handle noisy data as well as being able to employ a principled methodology for acquiring the knowledge necessary for disambiguation
similarly the value assumed by a case slot of a case frame of a noun can be viewed as being generated by a conditional probability distribution p nln s
the advantage of this deterministic approach is its simple mechanism while the disadvantage is that although it can output the most preferred interpretation it can not rank interpretations in their preferential order
alpp states that a phrase on the right should be attached to the nearest phrase on the left if possible and that phrases should be attached to a phrase in parallel if possible
for example in the sentences britain reopened the embassy in december britain reopened the embassy in teheran NUM the pp attachment sites of the two prepositional phrases are different
we feel that this result is very encouraging
when spoken language understanding is performed in a goal oriented dialogue system it is usually acceptable to strip off any extraneous information in order to map the speaker s intention onto an unambiguous system command
one of the traditional approaches to this area of pragmatics is to recognize speech act types compositionally using syntactic and semantic rules plus a few pragmatic principles such as felicity conditions for each speech act type
the speaker may also try to create distance between the addressee by avoiding reference to both the speaker and the addressee as in an agentless passive sentence i would like a reservation to be made
the use of pre established expressions helps both the speaker and the addressee since such expressions can be easily and quickly retrieved from their long term memory and little encoding and decoding work is required
in our approach we treat pragmatic strategies as additional information that are superimposed upon basic propositional content try to recognize and extract them and transfer them to the appropriate target language expressions
therefore the counter intuitive interpretation that jiro did not notice taro is obtained
on the other hand the whole sentence can be treated as a single unit
a 7f may mconm a ct later in the discourse
we can think of at least two ways to handle complex sentences
therefore the intrasentential ellipsis must be dealt with separately from the intersentential ellipsis
our simple method yielded the accuracy of NUM for the zero pronoun resolution
therefore the antecedents of tile zero pronouns in sentence b2 are identified as follows
recently therc have been a nmnber of works thai illod NUM ho
using this approach we need not modify the original centering algorithm drastically to handle complex senteimes
this is what i meant to say when i wrote in my title the power of words in message planning
given the fact that words can be used to specify non linguistic thought there is feedback from the lexical to the conceptual component
this allows the generation of the first element whew and the fact that there is a person who saw some event
both of them m e subgraphs of the utterance graph that is both of them express part of the message planned
the reason is simply economy more of the message is expressed by using the given resources hence time and space is gained
surprisingly this is all the more likely as we get close to the surface that is relatively late in the process
there has been very little previous work on treebanlc conversion
type relations are of course captured directly as the monotonic typed multiple inheritance network itself which structures the lexicon
such a component is tided to the inheritance machinery fbr independent reasons mainly because of the limited expressivity of the type systeul
vertical relations are more constrained becmlse they are based on componential analysis starting out dora the set of properties that objects have
only completion and scanning steps need to be traced back
it does not require rewriting the grammar into normal form
ps2 is the class of context free languages generated by grammars whose productions are restricted such that the lhs of each is a single nonterminal symbol and each rhs is a sequence of terminals and nonterminals
the worst case computational expense of the algorithm either for the
the re ognizer in educes a eonllected graph where each edge denotes a word hypothesis
as just described this component need not be present by the time of the channel creation request
typicmly when an interface between two subcomponents of a system is needed at irst very simple means e.g.
we listinguish two types of channels base channels re the primary acilities of communication
these split channels are achieved by dividing l channel into two endpoints one at each side of the channel
instead once the identity of the communication partners is established cornmunication between them is strictly local
before describing in detail the structure of a component we will point out the overall layout of an application
consider fig NUM as an exa mi e for what purpose split channels were used
we noticed that wordgri phs produced inerenmn tmly laity e tell tiilles larger than conw ntionally
in that case the ils notifies the source component of the event and communication c n take place norreally
the preconditions of an action are essentimly taken to consists of those specified by the ac tion s recipe and those of its component actions if atty
this method is based on active chart parsing and uses augmented edge structures to keep state information locally and time map management to deal with state changes
efects ea u ejy cts ed o and NUM lcb t t b e prcconditions e
for example a speaker may not perform any action to make a hearer believe a proposition if the speaker believes the hearer already believes it
imt lemented progl am uses an agenda inechanism that uses priority scores on edges to obtain i re fi rred plans first
since information on the effects and preconditions of the action represented by an edge must be kept locally we use the edge structure shown in figure NUM
this recipe thus says that an informref action can be performed by an action that has bel h p as its effect
if we are not permitted to use this form we must enumerate all the actions that achieve p together with the conditions under which they do
the proof of a contradiction from a given reading in a given context and our conceptual and world knowledge which allows us to rule out that reading
approaches to model the lexical disambiguation process formally differ as to the degree to which they consider the information of tile various sources needed to disambiguate properly
in order to illustrate this insufficiency let us consider the english translation of tile sister reading of NUM repeated in NUM
hoppe et al NUM p NUM lcb which is of course needed in NUM the conjunction of the roles sister and married
although we think that one needs a more expressive language for an adequate representation of discourse and that very ofl en nonmonotonic reasoning is involved the
in order to isolate tile consistent information pieces provided by a possibly inconsistent discourse we use a discourse representation and meaning postulates in clause form
assuml tion i is therefore as follows i we have to assume a fully expressive language for the representation of texts
thus our third assumption is iii lexical disambiguation is very often possible although the discourse is inconsistent or its consistency is not known
what we are in fact looking for is a procedure which tests whether there is a consistent set of information pieces of the discourse which contradicts mp
we define so that a semantic label sem lcb cl cn rcb is subordinate to each class ci vc c sem sem c when searching for classes which give maximum association scm e section NUM this detinition makes it possible to calculate association score for all the senses in a semantic label and to find senses which give a maximum association score
then given a bilingual surface case structure e the number of japaxiese case class frames f s which is superordinate to e i.e. e f f s is less than NUM deg x d an t the mtmber of possible pairs of c and f s is less than NUM deg x dt x df which is constant
one well known problem is that measures based only on the absence of crossing errors on sentence level such as sentence accuracy and viterbi consistency are not usable for parsing systems that apply a partial bracketing since a sparse bracketing improves the score
we do not intend to make any particular claims about these parsing systems nor about the treebank we used the test was not designed to draw conclusions about the treebank we only use it to discuss the issues involved in evaluation
consequently syntactical analysis has become an area with a wide variety of a algorithms and methods for learning and parsing and b type of information used for learning and parsing sometimes referred to as feature set
these evaluation metrics have a number of problems and in this paper we argue that they need to be reconsidered and give a number of suggestions either to overcome those problems or to gain a better understanding of those problems
if the differences between scores become too small in relation to the test set one will just be making a parser for the test set and the performance will drop as soon as other data is used
it may seem logical to find the proper values for m1 and m2 as a next step in other words deciding how many brackets were too easy and how many were too hard
we do not have tile space to go into tile details of the relations between the distributions but if a and b would behave like a binomial variable with test size n with pa and p as respective chance on success the distribution of yy should again be a binomial variable for test size n with chance pry papb
there is a strong variation between brackets because some brackets are very easy and solile are very hard
NUM he inheritance data shows that in our test crossing errors are often related p inheritance
the difficulty with conjunction is not with classification of the conjoined noun phrases it is easier as a matter of fact because they carry more evidences but with identification of the phrase itself because of the structural ambiguities it typically involves that can not be dealt with easily on lexical or even syntactic level
each piece of significant contextual evidence is then weighted against its distribution in the balance of the training corpus
u2 the man was angry with him
the targeted students are users of american sign language asl a language that is very different from english in its structure and discourse strategies
let t be the set of the types p be the probability distribution over t let m be the set of the messages and a be the set of r s possible actions
let i be the proposition that the sender s intends to communicate a semantic content c to the receiver r then i entails that s intends that r should both recognize c and believe i
cheap talk game is another sort of communication game
communication has been discussed in the gametheory literature
this is captured by assuming p1 p2
this explains the preference in NUM
secondly it takes into account the information of conjunctive postpositions that are between two sinlple sentences by classifying them into three classes
for instance if one is disambiguating a manual of some sort imperative readings of verbs are certainly possible whereas in normal plain text with no discourse such readings are discouraged
for every ambiguous parse in such an unambiguous context we count how many times this parse occurs unambiguously in exactly the same unambiguous context in the rest of the text
when the sentence transducer is composed with all the constraint transducers in sequence all possible votes are cast and the final sentence transducer reflects all the votes
this constraint application paradigm makes the outcome of the disambiguation independent of the rule sequence and hence relieves the rule developer from worrying about potentially conflicting rule sequencing
rules describing language specific linguistic constraints vote on matching parses of tokens and at the end parses tsuggested by lauri karttunen private communication
other hand this very nature introduces another kind of ambiguity where a lexical form can be morphologically interpreted in many ways not usually predictable in advance
we also employ a set of rules which express preferences among the parses of single lexical form independent of the context in which the form occurs
we thank lauri karttunen of rank xerox research centre in grenoble for providing the xerox two level morphology tools on which the turkish morphological analyzer was built
the words that are counted as unknown are those that could not even be processed by the unknown noun processor as they violate turkish morphographemic constraints
after all rules have been applied to all token positions in a sentence and votes are tallied morphological parses are selected in the following manner
this study also shows that the average number of punctuation symbols to be expected in a sentence of english is four thus reinforcing the argument for the inclusion of pnnctnation in language processing systems
NUM the shark whale and dolphin can all swim
the best data sources are parsed corpora
to deal with these two types of differences we propose lexicalisation mechanisms which proceed from the same semantic representation for both french and english realisations
for example coinpilation of xo yo z lnight yield xo y with term ay z az y
this name is used for expressing preferences on alternative rules cf
in figure NUM a temporal adjunct is represented under time adj
failure of executing some action causes the rule to be backtracked
i pc p NUM is set of words that co occurs with wiorw2
in isamap let us consider n exainph to see how mgorithm works
tim areas tha l occupy a large sl ace are preferred
does this mean that both speakers accommodated or that neither did
lexical choice is a surface level phenomenon open to manipulation
significance levels for differences in lexical accommodation two way anova
accommodation has also been shown to be present in human computer interactions
an adaptation of this algorithm will be briefly summarized here
all runs were performed on a sun sparestation NUM NUM with 128m real memory
th inl ut it ory s xluoncc
a pair of such close trees is depicted in fignre NUM
to detect such cases we use the notion ol a cnl off distance
a y initial sul scquonce of x shortor tha tl
tcrminat nodes during i he q rcb aych
we can convert this set into a trie data structure
from the methods based on conceptual distance sussna NUM is the most similar to ours
we use r NUM to denote a production in pl where the parentheses remind us that we are in a lig
we will only consider ligs such there is a bijection between its production set and the production set of its cf backbone NUM
a number of ways to implement this technique have been developed
the contribution of the lm depends on its vocabulary and perplexity
however the email lm performs better by NUM NUM
evidently the choice of vocabulary also makes an important contribution
this produced a mean rank of i25 NUM std
it measures the degree of monotonic association between two rankable variables
the correlation with the bnc as a whole was also measured
this was calculated using the following algorithm j
ar bt NUM NUM but repelling the sciences e.g.
the lexicon in use contains more than NUM NUM stem entries concerning morpho syntactic information
the general structure of a lq ench negative clause is therefore tp np t negp neg agrp agr vl advp vp a a
however there is no similar consensus about the position in d structure and the possible movements of the second element of french negation namely one of the adverbs pas plus point gust e etc
the pure temporal dinmnsion which is accounted for by the time referent t will be introduced at the node t the aspectual dimension which is accounted for at the semantic level by the discourse referent e s is associ
with this in mind negation can either be seen as an operal or always having a with scope over c vents statcs or s a kind of t st x tlta l
represent clauses and to build their drss we use a tree structure consisting of a verb phrase vp dominated by tile projection ip of a functional head i bearing the verbal tense and agreement features i stands for inflezion
nevertheless the categorisation into states s and events e of the utterances to bc represented and the possible introduction of the discourse referents of one of these aspectual category may be associated with the asp head
we associate verbs and nouns with NUM redicatiw drss for instance the verb voir see yields aa ky voir x y the role of which is to introduce predicates into the representation
the crutim a ssumption here is that prepositions have their own semantics an idea first exploited in lawron NUM NUM
therefore the information about the alternation is duplicated in the type system as it is encoded both on tile complement types and the verb types
first the type system is augmented to allow for declaring the property of being an optional or an obligatory prepositional complement as in figure NUM
we will exemplify ore approach by treating a subset of verb alternati ms which conform to the following general schema
at first sight then it appears that the benefit of adding an external lexical l ule component outweighs the disadvantages external powernl mechanisms
alternatively we proposed that lexical entries are descriptions of objects which allow for further contextual specification of their properties on the basis of clearly defined constraints
secondly identifying the semantics of verbs with ttmt of prepositions does not allow for expressing certain types of diverse behavior within the class of a lternating verbs
the parallel drawn above between vertical relatedness expressed with the type system and horizontm relatedness among descriptions of fully formed objects is however rather misleading
on the other hand horizontal relations among descriptions very often lnodelled by means of lexical rules are essentially relations holding directly between objects themselves
in contrast chart parsing with constituent boundary patterns can constrain the number of arc creations because only an constituent boundary creates active arcs while a variable e.g.
additionally by dealing with best only substructures the explosion of structural ambiguity is constrained and eflq icnt translation of lengthy input can be achieved
bus creates the passive are NUM and the passive arc NUM is created by combining NUM and NUM
if the whole input string can be covered with a passive arc the parsing will succeed and the derivation of the passive arc will be the parsed result
since the head of x at y is designated as x goes becomes the lead word for NUM
table NUM shows the head parts of the possible substructures for goes to chinatown at ten a m which corresponds to the passive arc NUM
in english to japanese translation the present vocabulary size is about NUM NUM words NUM and the number of training sentences is about NUM NUM
the source and target expressions of tile transfer knowledge in tdmt arc ext ressed by constituent boundary patterns which represent meaningful units for linguistic structure and transfer
the rest of it is divided into three parts
in this case the left fringe of the derivation is guaranteed to result in a terminal after finitely many steps but the derivation as a whole may never terminate
probabilistic parsing algorithms may also be classified as to whether they are formulated for fully parameterized cnf grammars or arbitrary context free rules typically taking advantage of grammar sparseness
therefore to ensure that item probabilities are correct independent of input position item sets would have to be constructed so that their probabilities are unique within each set
because of the imprecise relationship between lr probabilities and scfg probabilities it is not clear if the model thus estimated corresponds to any particular scfg in the usual sense
let c x a t NUM be the number of occurrences of predicted states based on production x a along a path NUM
the bracketing information constrains the parse of the inputs and therefore the parameter estimates steering it clear from some of the suboptimal solutions that could otherwise be found
the theoretical results of tit present paper c m therefore be straightforwardly integrated into a lexical rule compiler of the sort described by meurers and minneu in which applicability of lexieal rules is checked automatically under subsumption
formally this merging is nchic vcd t y al l en ling the comi s list of the governed v rh with l h olm ch mcnt list NUM hal
ain order to reduce the size of the feature structures prefixes of paths that begin with the synsem attribute have been omitted as much as possible in fig NUM and all other feature structures that are shown in this paper
since consensus on how to provide an adequate denote tonal semantics for i rs has not yet been reached it would go well beyond the scope of this paper to develop a fully worked out proposal on how to process lj lcb s
in iips l his can he achieved by structure shnring the complements of the ina in vcrt with the subcal egorization in form d ion of each mxiliary in i hc scnl ence
on relations between lexical entries l efinite clause schemata as in fig NUM for n NUM define relations between base les listed in the lexicon and derived l es that are obtained via the application of a sequence of lrs
NUM hypothesis b a lexical rule applies to a lexical entry iff the lexical entry is subsumed by the left hand side of the lexical rule without much argument it is cotnmonly assume t that llypothesis a is correct el
now there is an entry for loves v so a link vp NUM v NUM is needed since loves subcategorizes for a subject and an object
for example desu in futari desu u8 in figure NUM represents a speech act type response
with local cohesion turn taking part a using these endexpr bigrams a series of experiments was done for the closed data
this choice was also motivated by the observation that typically the most important constraints on grammaticality of the input are in the syntactic part while most of the semantics is purely representational NUM a straightforward way to achieve this is by inanipubating grammar rules and lexicon entries for the syn parser we recursively delete tile information under the sem attributes and sinfilarly clear the syn attributes to obtain the subgrammar for the sem parser
using the smoothing method we obtained a NUM NUM accuracy for the closed data and about a NUM NUM accuracy for the open data
table NUM shows the accuracy of recognizing local cohesion in three cases the turn taking case the no turn taking case and the total case
null however the most imi ortant disadwmtage of the conq ilation nmthod is that it no longer guarantees soundness that ix the sut grammar s might accel t ul terances which are ruled out hy tile flfll grammar
because a grammar contains finitely many rules of the above form and because the daughters the right hand side of the rule are type symbols and there are only finitely many of them a great deal of this partial evaluation process can be performed otttine
this rules out that the parsers should communicate by exchanging their analysis results in terms of resulting feature structures since it would imply that on each communication event the parsers would have to analyze the structures to detect changes whether a structure is part of other already known structures etc
in general ewm the intersection of the languages accepted by g v and g does not yield the language accepted by g only the weaker relation ps g c ps y o NUM g holds
since we can not assume fixed finite context boundaries within each lexical ambiguity can be locally resolved inference based approaches seem more promising for handling lexical disambiguation hfference based approaches assume thai the language of a logic is used to represent the meaning of a discourse that the same language is used to store our conceptual and world knowledge and that resolution is achieved on the basis of the underlying logic by special inferences
since the correct disambiguating inferences can not be performed anymore if the truth conditions of a discourse are boiled down in a way that allows to represent it somehow in such a restricted assertional language approaches which model lexical disambiguation on the basis of these knowledge representation formalisms must fail
with clause rel deactivated the system incorrectly selects the more recent vp discuss things
head match the system choice and coder choice have the same head verb
the antecedent vp selected is nourished mine as no other social influence ever has
this can be observed from the fact that composite performs better than its individual components
there is however a large body of empirically oriented work on pronoun resolution
also the subparts of the system are analyzed individually in three different ways
NUM the syntactic filter eliminates all vps that contain the vpe in an improper fashion
on a sample of NUM examples the results were as shown in table NUM
all the examples of vpe were coded for the correct antecedent by two human coders
the distribution of r as normal mean NUM variance i n NUM assuming independence is asymptotic for large enough samples and does not make any assumptions about normality
however the standard word order a sentence final verbal complex with the finite verb as the last dement is encountered only in subordinate clauses
in active contexts it becomes the subject and receives nominative case in passive contexts it may be optionally realized as a ppvon see fig NUM
another important step is the declarative definition of linguistic data grammar and lexicon which also fa ilit tes reuse in another setting
it applies in all cases ex el l dative NUM setting the umlaut feature which u iggers the two level rule forcing umlaut
while the original roof t hoh gy component of fuf ix geared towards f nglish ordy x2morf can be used with a wide range of languages
reusability is crucial for the successful application of n i p techniques to real life problems since it helps to cut down on both development and adaptation effort
l inan tim support for the austrian research institute for artiticim ntelligence is provided by the austrian 13undesministerium fiir wisscnschaft forschung und kunst
the other probh m was that the existing hps l inspired gramrna r of german could not be directly ported to the fuf formmisln
mad word w is to be minimized
the notion of structured complexity provides an explanatory account
figure NUM ent er l lnlmdding vs not elt e l hire th m their english couvd erpm l s p
for example the speaker s expectation of 5a is that jesse knows he can not be promoted betbre peter
speakers can use a variety of signals cues intonation contour exaggerated stress tone of voice hyperbole facial expression etc for implicitly communicating their emotional attitude
a e at c a since the part of its propositional content p and x are unifiable
however when the hearer does not recognize all components he she also ol tains new information that the unrecognize d
one way to huild a l robabilistie grammar is to specify what sequences of moves such as shift an reduce a parser is likely to make
model c was a version of model c that ignored lexical dependencies between parents and children considering only dependencies between a parent s tag and a child s tag
it we l hc s tnm signature if t hey are indistinguishal le i their m ility to otlll ill
w also give preliminary empirical results from evaluating the three models p lrsing performance on annotated wall street journal trmning text derived fi om the penn treebank
we sial ulate that the model discards fl om the popula tion tiny illegal structures that it generates they do not appear in either training or test data
when selecting other rules by virtue of a category a lisp function is called that 1the notion of template is preserved for historical reasons the predecessor system tg NUM was strictly template based identifies the relevant portion of the input structure for which a candidate rule must pass its associated tests
assume that n2 e q and call s the subtree of c at n matched by q s exists by lemma NUM
best xplaii l he dal a
the present research was done while the first author was visiting the center for language and speech processing johns hopkins university baltimore md
the airline conlpany will fly japan airlines
in practical applications t t and iti are very close of the order of the length of the input string
in this case s belongs to a p chain consisting of at least two subtrees and s is not the bottom most subtree in the p chain
the claim can be shown by induction on the position of m in a post order enumeration of the nodes of lhs r
to compute the translation m g we first visit the input tree with ag and initialize our data structures in the following way
this includes errors in pronunciation word selection and structure selection
when the speaker actually vocalizes the utterance speech performance errors may occur
noun phrase verb phrase chinatown a m goes a m morning a m depart a m v no x v ni x NUM NUM NUM NUM according to the distance calculation in the combination of NUM and NUM i no NUM with the distance value NUM NUM is selected as a target expression
example NUM gives the set of cell categories for the position structure grammar in example NUM
the first seeks to associate an elided construction directly with a semantic representation while the second mediates semantic interpretation through the reconstruction of the syntactic structure of the antecedent
NUM construct a new clause headed by a NUM substitute ord ph list for the list of arguments and adjunct phrases of a in the new structure
let s bl bk NUM k be a sequence of ellipsis fragments such that for each bi s b q immediately follows b i
syn iochlead j vfo rm fin subeat sl eomp dt s ll comp dtrs
by contrast the generalized reconstruction algorithm generates the full syntactic structure of the elided clause and so it provides the representation required to specify the contrast between 15a and 15b
take s to be maximal in that there is no ellipsis fragment b0 or bk NUM not contained in s which immediately precedes or immediately follows an element of s
we require this rule in order to allow for the fact that there is no apparent upper bound on the number of adverbs in a vp
this will permit the specification of the equations in 14a c for NUM higher order unification solves these equations to yield 14d the desired interpretation of NUM
another problem is posed by the fact that as higher order unification applies to semantic interpretations of antecedents it will not have access to syntactic structure
the result is a conjoined np which taken as a generalized quantifier applies to the antecedent clause interpreted as a predicate formed by lambda abstraction
most of the concept nodes represent general phrases that are likely to occur in a wide variety of texts e.g. x saw
we take the two sentences individually
there are two distinct effects on the output context
second xm is applied to the property p2
in the first sentence the center is tom
the coordinate words are finally considered the coo arguments of the conjunction which is assigned to the shared microsemantic case
the implemented tagger requires three transducers which represent a lexicon a guesser and any above mentioned approximation of an hmm
zero of ambiguous classes ca and ends with the first unambiguous class c of the sentence
often however it is unnecessary to decide on the tags of the whole sentence at once
for every state as many outgoing arcs are created as there are classes three in fig
this illocutionary value does not affect the lexicalisation of the proposition i e the construction of the deep syntactic tree
the corpus is composed of about thirty bilingual pairs of extended procedural texts extracted fl om aircraft maintenance manuals
in addition to the quantitative differences between the word to word model and the ibm model there is an important qualitative difference illustrated in figure NUM
for our bitext recall of NUM NUM and NUM corresponded to translation lexicons containing NUM NUM and NUM words respectively
many of these contributions are concerned with multilingual generation which is often presented as an alternative to machine translation
at present we consider the lexicalisation problem as a mapping process from conceptual representations to french and english lexemes
lems are studied by several nlg researchers eg NUM NUM NUM
in the example given in figure NUM the french and english versions are derived from the same conceptual representation
figure NUM in the english version the predicate and the instrument argument are mapped to a denominal verb
a study of some lexical differences between french and english instructions in a multilingual generation framework farid cerbah dassault aviation
the generation of sentence lie NUM will proceed from an input representation based on the superordinate conceptua l
entry alone and in such a case the potential for error would be a t least twice as high
the thematic grid th loc indicates that the verb has two obligatory arguments a theme and a location
however as we saw in section NUM the representations used here carry over to other languages a s well
we are currently using these lexicons in an operational foreign language tutoring and machine translation
the arguments for cause are thing NUM and go ident
i thematic roles without prepositions NUM example the flower decorated the room
several of these correspond to verbs that take sentential complements e.g. coerce
just as in the previous case whereas goal requires a different treatment
the input for this example indicates that the goal is headed by an unspecifed preposition
example NUM l eter broke his wife s favorite tea ul when he washed the dishes awkwardly
the state of allah s that an action a is perfbrmed is expressed by did a rcb rcb
we would like to introduce lexical information for the preposition in by way of illustration
on the basis of these variables other variables can be defined such as in NUM
when more tha n one domain NUM lan is possible it chooses tile domain i lan that requires the shortest execution time
e nom go out pol nonpast
sentences which have the expressions of ability or permission mean not
the exceptions are only about NUM of all examples
table NUM our estimate of the usage of tile matrix
table NUM NUM and NUM show the distribution of usage
default NUM subject of sentem e with to or
next we consider the usage of tara and nara
these phenomena probably due to the nature of each conditionals
like the speaker the hearer and so on
qsb nom go ollt car i ol nonpast
ptfis relation is useful for the incremental strategy since it allows speakers to begin uttering even when the content has not been fully determined
however it remains far behind the NUM parse accuracy of dop1 for part of speech strings for the same subtree depth see table NUM NUM
in the absence of morphological annotations in atis a dictionary can provide the lexical categories of both known and unknown words of an input sentence
moreover the results of a less than optimal version of dop on the wall street journal corpus suggest that the approach can be succesfully extended to larger domains
moreover our results are still based on a limited version of dop since for time cost reasons no subtrees larger than depth NUM were used
although bfill does not report the parse accuracy of his system we can derive that dop1 does better on the bracketing accuracy and the sentence accuracy
new input is parsed by combining tree fragments from the corpus the frequencies of these fragments are used to estimate which analysis is the most probable one
thus in order to calculate the adjusted frequency of an unseen type one needs to know the total number of types in the population
one of the goals of the experiments was to decide among different variants of the conceptual density formula
instead on our experiment we use saliency values NUM based on the lexicographic file tags in semcor
we conducted some experiments in order to test the effectiveness of this strategy
as a method of statisticm estimation mdl is guaranteed to be near optimal
finmly ld t m is calculated by
note that the coverage of mdl thesaurus signifiea ntly
but when we update our data structures with the procedure update we also look for matchings of lhs ri at nodes of c in chain i
that is as mlt is the conditional probability of s s sending message m provided that she is of type t and an aim the conditional probability of r s doing action a provided that she has received m
for each active pair the body of the for loop in the mmn program and the body of the update procedure are executed taking an amount of time o p
for t e e t let alsot t be the total number of rules that are successfully applied on a run of algorithm i on input t counting repetitions
figure NUM action schemata and decomposition methods for proposing domain action r8 is decomposed by applying the action schemata and decomposition methods shown in figure NUM
in such cases motivation occurs together with circumstance and contributes to the incrementa NUM strategy in the same way as circumstance
finally motivation is mainly used for describing a domain action as a nucleus and motivating addressees to adopt the action by presenting a fact as a satellite
by applying r6 and r7 to the initial communicative goal rs the following utterance plan is obtained for an action schema
to describe such a domain action with verbs such as iku go it must be in focus
a heap data structure h is also used to order the indices of the non empty sets rule i according to the priority of the associated rules in the rule sequence
as for phenomena peculiar to st oken dialogues they focused on tlesil c ltion an l self tel air
when domain objects are linguistically realized by the surfaee desc obj in r14 pragmatic constraint c2 is exploited to t ronominalize focused objects
in addition according to constraint c3 the objects that are not in focus need to be topiealized if they must be in focus
lcb n rcb measure NUM nkutib measure NUM
this is illustrated here by analyzing measures NUM and NUM of the arabic verb
notice that the vowel ill the affixes of measures NUM and NUM is a variable v
when n NUM the parentheses can be ignored hence x and x are equivalent
NUM lcb t rcb verb all ix measure NUM the additional two level rules are
one of the extensions introduced in the multi tape version is that all expressions in the lexical side of the rules i.e.
measure NUM kutlib is derived by prefixing a mora to the base template under ni c
the methodology allows succinct analyses of phenomena such as infixation and reduplication that were difficult to describe under sets of transformational rules
even so corr t resolution of a NUM ronoun r f r ul is iml ortanl for disambiguating the word sense f a NUM r di al modified NUM y t he l roiiou11
excel t for information it the sequence of senl en es olir framework does nol consider any discourse stru ure mwh as the discourse cessmg a gloup of sentem s togethel makes
proces ing l ett od m mely filll t x processlng f6tioi ex rgcted from other senten es within tlm same using on its effects on ttie output of a nmehine trails null lation system
in our view any effective evv luation methodology for automatic grammatical analysis must confront head on the problem of multiple correct n wers in tagging and parsing
in our current research we are emphasizing the creation of decision tree questions for predicting semantic categories in tagging as well as continuing to develop questions for syntactic tag prediction and for our nile name prediction model
the atr english grammar is particularly detailed and comprehensive and this both helps in parse prediction and enhances the value of output that is correctly parsed by our system
alternatively we can tag the entire sentence first then work from tags up left to right which also yields a unique derivation for each parse state
a grammar based probabilistic parser is described and experimental results are presented for the parser as trained and tested on a NUM NUM word highly varied treebank of unrestricted english text
the way we evaluate our tagger is to compare its performance to the set of correct tags for each word of each sentence of our gold standard test data
NUM NUM of the parallel data NUM NUM words aligned essentially perfectly and for the work reported here we decided to operate only on this satisfactorily aligned dat
attachment sites are available in the grsmm r are used precisely in the preeba k and are required to be handled correctly by our parser for its output to be considered correct
determinerless noun phrases tend to have different chances of occurring in certain gt rnrnatical constructions than noun phrases with determiners and this feature makes it possible for our models to take account of this tendency
a used by misr solution of NUM ronoun ref r nts v n if the probability of misim rl r tation is less than 10j
however as shown i NUM figure NUM which is a translation output of an actual oml uter manual we can often find modifier modifiee relationships that lisambiguate structurally anlbiguous phrases in tile sltme context at least in technical documents
in figure NUM the ambiguous prepositional NUM hrase of a job NUM in sentence NUM is disamt iguated and attached to the flow l y of noun may modify verb as in he robbed a lady of her money
the fl us of focusing sul jun ts ix resolved by means of the following algorithln NUM find among the NUM revious sentences in the context model one that contains expressions morphologically identical with those in the sentence containing the focusing suhjunet
because the factor categories must be contiguous and in order this amounts to considering each of the ways in which the substring can be split into two pieces
each of these operations involves filling a cell for a target category by using the entries in the cells for two factor categories
these connections may be derived from work in language assessment and grade expectations such as found in ber88 lee74 and cry82
for example in krashen s monitor model of sla kra81 kra82 he describes the learning process as a series of language level attainments
for example if the student s educational program stressed subject verb agreement this feature could have already been learned even though others before it in the original sla model may remain problematic
thus language transfer might explain why an asl learner of written english may have difficulty leaming which word other or another is appropriate to use in english
the grammatical markings for topichood involve raising the eyebrows tilting the head and maintaining fairly constant eye gaze on the addressee unless directional gaze is needed for other grammatical purposes
for example in asl it is usual to establish tense at the beginning of a discourse segment or time frame and then not to mark it again until the time frame changes
the eventual system will analyze a text written by a student identify errors in the text and engage the student in a tutorial dialogue aimed at some subset of the errors identified
for example one would expect that the group of features that differ between a first and second grade reading level should be acquired together i.e. between first and second grade
what we do propose is that a language model that accounts for possible effects of the li on the l2 should be developed by comparing the two languages on a feature by feature basis
for every clement of the agenda all possible binary combinations of the syntactic categories are tried failure driven loop
a t l is quite valuable for several kinds of corpus studies concerning the medical vocabulary co occurrence patterns statistical data
it seems then that the structure needed for conjunction reduction is some generalization of the standard structure used for coordination of constituents
the constituents may be of the same category NUM as well as of different categories NUM NUM
both parts have been encoded within hpsg using the same resource that is the subcategorization and its principle which we have just extended
i ask for the bill and for someone to pay 2e e rends addition et que quelqu un paie
this makes the algorithm highly parallelizable
tlmrc are two main reasons for that
usual updating flmctions are the following
see torras NUM for a clear exposition
searching a more specific support fllnction
the denonfinator expression is a normalization factor
alternative stopping criterions will require further attention
document cl sification has been widely investigated for assigning domains to documents for text retrieval or aiding human editors in assigning such domains
this in turn leads to selection of some contexts to serve as indicators of relevant entities in other words they become the initial rules of the emerging spotter
depending upon the current information needs one may be interested in finding all references to people locations dates organizations companies products equipment and so on
l in l h ll oil we restrict tony further t rocessing on these sequences and their contexts
further experiments rare required to deterlnint the level of preprocessing required i o optinfize the t erforlnanee of the hfiversal sl otl er
the type aer denotes sets of pairs of objects where each pair behaves as a complex object in discourse structure
all these objects are introduced on the semantic level and correspond to a number of objects that will be realized in syntax
the next step in the research is to organize the remaining classes into knowledge representations that relate their senses to each other
here i introduce two kinds of dots closed clots connect systematically related types that are always interpreted simultaneonsly
in corzlex homonyms are simply assigned two or more underspecified semantic types that need to be disambiguated in a traditional way
although homonyms need to be tagged with a disambiguated sense this is not necessarily so in the case of systematic polysemy
meronymic information is obtained through a translation of various vp and pp patterns into has part and part of relations
an additional measure of the effectiveness of the classifter is measuring the recall on classification of all nouns known and unknown
that is if the second head noun is of a dotted type and the first is of one of its composing types
from these one can filter out those classes that have only one member because they obviously do not represent a systematically polysemous class
as shown in i igure NUM pangloss has a large size and covers a good range of l het m
the total number of primitive labels in a world model is not a useful measure of the semantic cov null erage of a system
all oft quoted objection to having deep semantic overage is the dilliculty in scming up such a system along the dimension of size
that having good coverage along one or two of the three dimensions is not good enough for meeting the long term goms of ni p
llowew r there is little information only taxonomic and partonolnie relationships in each concept in its sensus ontology
we hope the preliminary proposals made in this article will lead to prolonged debates in the field and will continue to be refined
rent att t l esired l ireetions domain and task del endent ai style schema based nlp systc m
depth and breadth of knowledge necessary to cover a wide range language phenomena are at least as important to nlp as size
itowever we understand that the following are not complete or unique they are representative of the types of issues that are relevant to measuring semantic coverage
for some path p1 not defined at node n there are two cases to consider either p1 is the extension of some path defined at n or it is not
however the issue raised with respc ct to the cei rg in this p tper is orthogonal to overall assumptions of german obtuse structure
at first glance this seems like a possible way out since the cases that we havc considered problematic for applying the lr under unification involve les of auxiliaries
i rom a computational perspective such spurious ambiguities are highly undesirable since they force the parser into considering multiple analyses where a single analysis suflbes
this ambiguity is of course totally spurious since it does not correlate with a difference in semantics or any other relevant linguistic property of the sentence
no such problems of overgeneration or spurious ambiguity arise if a lexical rule applies to a giwm lexiem entry iff the lexical entry is subsumed by the
NUM igure NUM analysis tre e for sentence NUM tence NUM the tree in fig NUM illustrates the perco
verbs such as verstehen know how as in er versteht parser zu implementiercn he knows how to impleinent parsers
tags in the pplr is a shorthand notation for identity of the two categories in all respects except for the case value
wall street for exalnple appears NUM times in NUM longer candidate colh cations and NUM times by itself
compilation of weighted finite state transducers from decision trees
the rules are the only means to specify the interaction between agents and blackboards and between blackboards and user
tcleval displayquestionstextl NUM setnewtoplevelslots obj concrete waitforevent event textinp ut tcleval undisplayquestion setoldtoplevelslots0 set lcb obj path rcb i dst parse eti text reevaluate
we designed a multimedia grammar made up of a series of operators that relate media syntax and semantics
similarly outputs from prolog were converted to strings of text that were enunciated by a speech synthesizer
the graph in figure NUM illustrates this process by tracking two of these constants through a sample run of the system
consider that we have aa in some environment
tltxtin n n dialogue figure NUM adding learning module to an existing system
if no match is found the system could also ask for a repeat of the spoken input
the purposes of the interaction are to gather the missing information and to eventually achieve the top most goal
the paper describes problems in disambiguating the morphological analysis of bantu languages by using swahili as a test language
fourth the majority of bantu languages have a tone system but rarely this is indicated in writing
further such data is supplied for prediction purposes by thousands of questions about raw words expres sions and the sentence as a whole
effective questions about words and expressions for the purpose of predicting the semantic portion of the lexical tags are essential to the success of our models
in this paper i shall discuss the points one and two by using swahili as a test language
there are word forms with more than NUM readings tile largest number in the corpus being NUM readings
in corpus NUM the high percentage of four ways ambiguous readings found in corpus NUM does not exist
fortunately incomplete parse states are assigned probabilities which can be used to guide a search by r ling out unlikely parses without constructing the complete parse
the navigational commands are recursive so that for example one can arrive at a grandchild of a node by asking about a child s child
the ambiguity resolution is based on the constraint grammar formmism which allows tile use of grammatically motivated rules
so far about the cg rules reduce the number of multiple readings so that optimally only one reading survives
swatwol uses a two level rule system for describing morphophonological variation as well as a lexicon with NUM sub lexicons
NUM if a prepositional phrase or noun phrase is found in step NUM and a phrase of the same type is found in step NUM then remove the phrase found in step NUM from the antecedent
the most common situation NUM cases as in the above example was subdeletion when the vpe structure contains a noun phrase or prepositional phrase which substitutes for a corresponding structure in the antecedent verb phrase
these cases can be further divided into NUM too much material included from the antecedent NUM not enough much material included from the antecedent NUM discontinuous antecedents and NUM miscellaneous
if the above prepositional phrase in this new image of america were parsed as part of the verb phrase as indeed it should have been then the algorithm would have derived the correct antecedent
example excerpt from penn treebank produce humorous effects in his novels and tales as they did in the writing of longstreet and hooper and harris vpe did vpeal s antecedent produce humorous effects in his novels and tales coder s antecedent produce humorous effects normally an entire verb phrase is selected as the antecedent
example excerpt from penn treebank but even if we can not see the repulsive characteristics in this new image of america foreigners can vpe can vpeal s antecedent see the repulsive characteristics coder s antecedent see the repulsive characteristics in this new image of america by default only text contained by the selected verb phrase is included in the antecedent
in this section an algorithm is described to reduce the errors in error category b NUM caused by subdeletion
example excerpt from penn treebank representing as i do today my wife vpe do vpeal s antecedent representing coder s antecedent representing my wife this situation is similar to b2 in that the antecedent is incorrect because text not contained by the selected verb phrase should be included in the antecedent
more significantly the algorithm also assumes that analogous phrases following the antecedent and vpe always implies subdeletion
an algorithm to handle one of these problems the ease of subdeletion will be described and evaluated
using tgl small task and domain specific grammars can be written quickly
in figure NUM the righthand side constituent np is assigned accusative case
in the sequel technical aspects of the backtracking regime are discussed
it would be desirable to see the solution fulfilling most criteria first
correspondingly categories are used for rule selection see below
test rules select a rule apply the rule output string
partial reuse of such descriptions depends on whether the grammar writer keeps general
in addition it can be extended to cope with weighted criteria
tg NUM would give preference to derivations leading to the maximum global weight
the fundamental principle underlying all discourse generation is that there is always an addressee so that there is always at least an implicit potential for dialogue
the class of act to fill n comes from the network tor act class and in our case rule NUM NUM inserts inform after act
on re entry to the network to fill n the preferences have been reset to act and nucleus act
the concept of thematizing an element in the structure of a unit comes from systemic functional grammar where it is mainly associated with the clause
for the birmingham school of discourse analysis a dialogue consists of a series of transactions each of which is made up of a number of exchanges
the sfm is more traditional in that its structure is based on constituency rather than the concept of sister dependency as in rst
the answer seems to be that genedis which is itself adapted from the sentence generator genesys provides an appropriately rich and relevant array of o perators
NUM NUM t satellite unthematized s NUM for s prefer act satellite act for s re enter at discourse unit
model of dialogue structure with the relations of rhetorical structure theory pass through the netw ork the class of the act will be an elaboration
many of its choices are guided by the goals set in the higher planning component but much of the discourse potential is captured here
even the intrasentential ellipsis can be handled with the centering theory because different simple sentences contain the antecedent and the zero pronoun respectively after partitioning
the enulneralion is performed in cases where both sentences haw zero pronouns and only e ither sentence has athe first sentence in a discourse has no ca
t m ormmw is omtmlx l bas xl m i h se answers
if a certain element of c i ui NUM appears as a zero pronoun in ui then so is cb ud
we also presented tile evaluation of our framework with real discourses although the evaluation is not so largescale to assert the effectiveness of our framework
since the field of ai which deals with knowledge represenl ation and rel rievm has heen worrying about the same problem for quite a hmg time it is not surprising that approaches to eope with phere is of course another procedure which is dual to the given one
the determination of the contextual appropriatehess of a reading of a lexically ambiguous sentence is conlmonly called lexical disambiguation
lexical disambiguation presents a particular problein for any sort of natural language processing but especially for machine translation since the seinantic grids of the source and the target may diverge in such a way that one has to disambiguate in cases where it is not required for applications of the source alone
can he ruled out since according to our conceptual system nobody can be married to his sister
null c es gibt keine schwestern aber einige arzte haben eine mitder sie verheiratet sind
we st ecify a class of inconsistency l roon which contains the disamt iguating inferences as a subclass
these two readings are represented by the two oversimplified predicate calculus forinulas given in NUM
example NUM is of course just beyond the border of the permitted expressions since it is in principle expressible but not allowed and much more problematic e.g. for donkey sentences is certainly the fact that variables are not explicitly available in these representation languages
h conceptual knowledge is represented by a finite consistent and decidable set of meaning postulates mp that does not contain logi ally valid subsets of formulas s 8since this condition is certainly not satisfed by our world knowledge its integration in the disambiguation process would he a much harder prohlem
it is ohvious that sentence la is much mor difficult to understarld than NUM b
we will also show that extrapositions such as heavy np shift and pp extractions are motivated by reducing syntactic complexity
using structural complexity instead of sentence length allows the read fl ility of documents to be measured tnorc accurately
we used the total length of the dci endency links in the definition of structural complexil y
the dependency relationships in tim x bar structure in l igure NUM are shown in figure NUM
i hc arguments presented in previous s l ions arc preliminary
the author is very gr teful to the reviewers who pointed oat several mistakes in the draft
i yet extr31 osition is good in NUM but b3d in 7b
in our work we take the view that important pragmatic information is actually encoded in many of the characteristics of spoken language that have been viewed as defective or ill formed
of course in english tense is marked on the verb in every finite clause so some tense markings in english may seem redundant to an asl signer
because of the differences in grammar and modality between asl and english we have attempted to abstractly characterize how languages could differ in a way that is independent of the grammar components
in deciding what to say the system s generation can be tailored to focus on those errors that involve language features that the student is in the process of acquiring
in this paper we discuss how generation issues affect the design of a computer assisted language learning tool designed to teach written english as a second language to deaf users of american sign language
thus it gives us a glimpse of the user s generation process by zeroing in on the mal rules we expect him her to be using at this point in their acquisition of english
in other words additional information about each student s language usage gathered over time should provide a better and more accurate reflection of the current set of language features they are acquiring
in particular his her placement would indicate that he she is still in the process of acquiring verb morphology mistakes of the kind given in this sentence are rather common for this writer
this body of research outlines sets of syntactic constructions language features that students are generally expected to master by a certain point in time
null once the slalom model is complete for the population under study presumably we will have a model of the order in which we expect our asl users to acquire written english
the ordering within feature hierarchies has been the subject of investigation in work such as ing89 db74 and bmk74
andreas stolcke efficient probabilistic context free parsing NUM NUM coping with recursion
in our previous corpus analysis applications any expressiveness limitations were easily tolerable since degradation was graceful
the expressiveness benefit is that a wider family of probability distributions can be written
in contrast the id lp work was directed at parsing a single language with free word order
then the maximum size of e s t is proportional to t s
the new algorithm in our experience yields major speed improvement with no significant loss of accuracy
the latter two kinds of productions allow words of either chinese or english to go unmatched
but the structural constraints of the btg can improve search efficiency even without differentiated constituent categories
in the present translation application any expressiveness limitation simply means that certain translations are not considered
the asymptotic time complexity for the translation algorithm is thus bounded by o t7
a similar situation holds on syntactic level
NUM NUM NUM NUM NUM NUM NUM input dieser erfolg ueberrascht in zwei hinsichten
the project started with a corpus investigation
in this section we would like to address the topic of efficiency
keys allow for indexation and efficient retrieval of rules and lexical entries
functor l f ead ii the functor macro is highly general
this was also necessary from an efficiency point of view
semantic reading distinctions thus are put into the semantic refinement lexicon
it composes the restriction list with the restriction list of the modified item
NUM decoding semantic and syntactic constraints are simultaneously available during the decoding process the decoder searches for parses that are both syntactically and semantically coherent
in other cases all the rules of a ruleset are triggered sequentially
now since p w is constant for any given word string the problem of finding meaning 34o that maximizes
for example the instructions needed to create the frame shown in figure NUM are NUM create an air transportation frame
some key advantages to statistical modeling techniques are all knowledge required by the system is acquired through training examples thereby eliminating the need for hand written rules
the individual modules are integrated through an n best paradigm in which many theories are passed from one stage to the next together with their associated probability scores
these scores are combined with the pre discourse scores p m s t p w i t already computed by the semantic interpretation process
instead i use contextual informati n in the target language to determine the is of sentences in the target language
i believe that we can not rely upon cues in the source language in order to determine the is of the translated text
this could create problems in making incorrect assumptions on words
section NUM examines different techniques used to obtain lexical probabilities
however choosing jmp only gives us NUM NUM accuracy
table NUM influence of context for n gram genotype disambiguation
there are three main differences between their work and ours
let f be the total count of the genotype bigram
we only need to estimate two parameters m1 being the number of bracket pairs that is discarded because they are always reproduced and m2 being the number of bracket pairs discarded because they are not reproduced
the last four items only apply to a comparison of two parsing systems for example two modifications of the same system here referred to as a and b
as an example of the last problem think of the indicated bracket pair in the sentence the dog waited for his master on the bridge
false error shows that almost i out of NUM crossing errors is not really wrong which indicates there is much difference in bracketing style between the treebank and the parsing system
pinh parse error inherited the number of bracket pairs produced by the parsing system that constitute a crossing error and have a direct parent bracket pair that also constitutes a crossing error
tinh treebank inherited the number of bracket pairs in the treebank that were reproduced by the parsing system and have a direct parent bracket pair in the treebank that was also reproduced
and how many brackets that the parser produces are not in the treebank nor constitute a crossing error and how many of those are not acceptable to humans
let observed yy in real test e yy in real test real test size x pa in real test x pb in real test then we get
this makes quantitative evaluation very important but the current evaluation methods have a number of drawbacks such as arbitrary choices in the treebank and the difficulty in measuring statistical significance
in this appendix we give a proof that the existence of these inverses is assured if the grammar is well defined in the following three senses
NUM their method uses the transitive but not reflexive closure over the left corner relation pl for which they chose the symbol ql
subject to these approximations then a probabilistic lr parser can compute prefix probabilities by multiplying successive conditional probabilities for the words it sees
one advantage over the grammar modifying approach is that it can be tailored to use various criteria at runtime to decide which partial parses to follow
essentially the same method can be used in the earley framework after extending the definition of outer probabilities to apply to arbitrary earley states
once the final state is reached the maximum probability parse can be recovered by tracing back the path of best predecessor states
it shows how to obtain the sentence and string probabilities we are interested in provided that forward and inner probabilities can be computed effectively
this will allow us to talk about probabilities of derivations strings and prefixes in terms of the actions performed by earley s parser
for completed states the predecessor state is defined to be the complete state from the same state set contributing to the completion
an unconstrained earley path or simply path is a sequence of earley states linked by prediction scanning or completion
only the par uneters relating to tile specific triggering rule ue therefore really stored
each of the four components of nkrl is characterised by the association with a class of basic inference procedures
we supply afterward some sketchy information about the inference techniques and the nlp procedures associated with this language
mnltiset ccg captures the contextdependent meaning of word order in fnrkish by compositionally deriving the predicate argument structure and the information strnctm e of a sentence in parallel
each nt l era nce in a discom se is associated with a ranked list of discourse entities called the forward lookiug eent ers
note that for cases that are not l valid but where the translation is linear logically valid deduction will fail due to unification failure for string position labels
by recording this index on tile argument position from which the additional assumption was generated we can enforce the requirement that the assumption contributes to the derivation of that argument
let us t egin by avoiding this latter l roblem by considering the fl agment involving only first order fbrmulae i.e. those defined by s fl
tim one shortfm1 here is thai tim the hod allows the impli al ioiml o spe ify t ha t
polarity applies also to subformulae i.e. in a formula xo y with a given polarity p the subformula x has the same polarity p and y has the opposite polarity
the language does not apply to single brackets but to their types and allows them to be only in the following order atlnt NUM a if all aani NUM the compositio NUM u1 NUM we would have an overlap of the two substrin s a u3 l and u1 NUM which have to be replacea
for every single replacement lcb ui NUM i ii li ri rcb we introduce a separate pair of brackets i and i with i 1e me if upi et lcb is identical with the empty string and i ff n if uppei t does not contain the empty string
with more than one label actually stands for a set of arcs with one label each figure NUM shows the state diagram of a transducer resulting from NUM or NUM
similarly a specitied context such as x y is actually interpreted as x y that is implicitly extending the context to infinity on both sides of the replacement
the second transducer in NUM which is an indicative lexicon of jr verbs concatenated with a sequence of at least one tag provides the indicative form and keeps the initial subjunctive tags
an nt pair a b can be thought of as tim crossproduct of a and b the minimal relation consisting of a the upper syml ol and b the lower symbol
if we permitted sequences like 11z ll u2 NUM we would also have an overlap of the two replacements which means we could either replace NUM u2 u or lu lle but not both
regular expressions based on NUM can be abbreviated if some of the ut NUM eit i owf i lcb pairs and or some of the lei t i lcb igiit pairs are equivalent
clearly our approach extends to cases of a tverbial quantification
focus is a much debated notion
analysis then proceeds further and the ground
see the appendix for the first three test texts
also they can be implemented successfully
the glass box method is concerned with examining the internal working of individual components in a system while the latter looks at the behavior of the input and output to the generation systems
each anaphor position in a generated text was left empty and all candidate forms of the anaphor including zero pronominal and full or reduced descriptions were put under the empty space
the comparison result is shown in table NUM
figure NUM rules used in the comparison systems
figure NUM a chinese anaphor generation rule
section NUM NUM explains how to re estimate the model parameters
raelamed unagi c is upenn edu
was induced using a simple bilingual concordancer
seminput hction token i llloc value imperative domain predicate fill agent object token NUM domain object operator i referential status specific patient object token NUM domain object hydr reservoir NUM referential status specific
in sentence NUM there is the fact of saying something and that of forming a joint venture
this research was supported by at
instead the japanese speaker may use the negative form x shire itadakemasen ka
figure NUM link type precision with NUM confidence
for many applications this is the desired behavior
according to pawley and syder a native speaker has a number of fully or semi lexicalized morpheme sequences in her long term memory in addition to a set of productive syntactic rules
assume that the following simplified f structures a c and i are associated with fl and if the graphical representation of which is given in figure NUM on the previous page
they ensure e.g. that verbs are subordinated with respect to their scope inducing arguments that scope sensitive elements obey the restrictions postulated by whatever syntactic theory is adopted that potential antecedents are scoped with respect to their anaphoric potential etc below we list the basic cases clause boundedness the scope of genuinely quantificational structures is clause bounded
after lpr fails to work the former interpretation is to be preferred according to rap because a man is closer to in chicago than phone in the sentence
the class sequence is now deterministically mapped to a tag sequence fig
due to lexical restrictions our average sentence length NUM NUM is below the one used in spatter and bld NUM NUM but some of our test sentences have more than NUM words and while the penn treebank leaves many phrases such as the new york stock exchange without internal structure our system performs a complete bracketing thereby increasing the risk of crossing brackets
while many limited context statistical approaches have already reached a performance ceiling we still expect to significantly improve our results when increasing our training base beyond the currently NUM sentences because the learning curve has n t flattened out yet and adding substantially more examples is still very feasible
in order to limit the size of the required lexicon we work on a reduced corpus of NUM NUM sentences a tenth of the full corpus that includes all those sentences that are fully covered by the NUM most frequently occurring words ignoring numbers etc in the entire corpus
we have extended their work by significantly increasing the expressiveness of the parse action and feature languages in particular by moving far beyond the few simple features that were limited to syntax only by adding more background knowledge and by introducing a sophisticated machine learning component
since the system has all morphological syntactic and semantic context information available at all times the system can make well null based decisions very early allowing a single path i.e. deterministic parse which eliminates wasting computation on dead end alternatives
a set of parse examples as already described in the previous section is then fed into an id3 based learning routine that generates a decision structure which can then classify any given parse state by proposing what parse action to perform next
for this purpose a text editor is currently provided
tile text planner then specifies the detailed elements of the
they kequently have no input or her than the software itself
there was a clear need for facilities to ease the input task
this interface allows the author to specify the procedure in several ways
its output also includes declarative specifications of all the widgets
authors can also invoke tile drafting tool from the graph
typed feature structures in the semantic level representing noun phrases can be seen as partial descriptions of objects each of which is compatible with the description
english right branching slmclural complexity NUM i igure NUM cross s ri31 dependency vs ceni er
if the iscompatible0 predicate fails the evaluation of this rule is aborted and other rules apply to process the text entered on the orthographic level
variables are typed in the sense that they impose an informational lower bound on the type of the feature structures with which they will be substituted
it is was shown that the accuracy of the syntax semantics interface in hpsg grammars in general and the empirical adequacy of binding theory in particular are improved by allowing the ohliqueness hierarchy to have a branching configuration
one simply has to state that for those languages like lango that have subject oriented reflexives the binding obliqueness hierarchy is not as sketched in NUM a but as in NUM b
NUM a objective voice torus saw ria now the examples in NUM show all the possible occurrences of one reflexive np in the basic transitive structures illustrated in NUM
nv see pm ria pm torus torus sees saw ria in toba batak there is strong evidence that in transitive constructions a verb and the following np form a vp constituent regardless of the voice chosen
the pairs a a and b b simply exhibits different surfhce orders of the oblique complements in the sentence a grammatical possibility illustrated in NUM a a
this is so because given the second assumption that non linear ordering are acceptable new cases must be taken into account namely those where the relevant elements do not precede each other in the hierarchy
zhangsan from l isi place hear wangwu not like self zhangsani beard from lis j wangwu k does not like himi j himsel
the information in the type hierarchy not only provides the types for the feature structures and defines the relations between them but serves also to restrict variable substitutions in the rules described below
essentially describes a synchronized NUM pair consisting of a left hand side english cfg rule called a source
thus monotonicity of head constraints holds throughout the parsing process
NUM he painted the walls and the floor white
the pattern p is said to be more specific than q
syntactic preferen e the sul ject NUM re is to i c col illltc
act e give a object e o arecipient e a a time e i d e
then the weaker t referen c NUM is rel r cl ed lo woid contradiction and the stronger preference NUM is survived
for exalnple a preference if al sees a2 then a2 and al are not equal means that the following expression shouhl be satisfied as nmch s possible
so if ther are yet multiple possible readings as a result of disambiguation we can keep these possit le readings as multiple i retb rat le
we a so dis uss an imt hmmntation of l rioritized ircums riplion by a hiera rchi a l
isa a male a in the sentence i a ain thc sentcnce i NUM he d eq a he NUM
the outputs of the case based sub networks as well as the final priming excitations are then calculated through a maximum heuristic
subordinate clauses provided the dependent clause is not a relative one the subordinate verb is subcategorized by the main one
linguistic coverage although our parser is dedicated to french applications we expect our semantic approach to be easily extended to other languages
are indeed processed correctly by the microsemantic parser whereas the lfg s accuracy is limited to NUM on the two first corpora
the priming process is carried out by an associative multi layered network figure NUM which results from the compilation of the lexicon
the principle of relative completeness is motivated by the high frequency of incomplete utterances ellipses interruptions spontaneous speech involves
on the other hand the relational priming identifies the lexemes which share a microsemantic relation with one of the already uttered words
the linguistic coverage of this parser as well as several its robustness have clearly shown the benefits of this approach
c ok x t with c0k t 0ma x if is consistent with the preposition k
the acquisition of case frame patterns normally involves the following three subproblems NUM extracting case fl ames from corpus data NUM generalizing case frame slots wmfin these case frames NUM learning dependencies that exist between these generalized case frame slots
first it is non compositional because tire meaning of the deaccented material in proper soes is solely defined by the meaning of its antecedent rather than the meaning of its parts
or smfll n the thr shohls will b large and ibw nodes icn i NUM o NUM c iinlccd rcsuliillg ill a sil lpie mod l ill which most
what seems to be true in practice is that some case slots are ill fact dependent but overwhelming majority of t hem a re independent due partly to the fa cl that usually only a few slots are obligatory and most others are optional
it scorns sail lo say lhere ore thai the dendroid utodcl is more sttitablc i or rcprcscnl ing the ira model o case flames than l w hmq emlcnl s ol
again we found that for the part of the test data in which dependency is present the use of the dei endency knowledge can be used to improve the accuracy of a disambiguation method mthough our experimental results are inconclusive at this stage
if on the other hand we a ssume that the random variables are independe l we only need to calculate and compare t x ih telescope and pgi t with
in the absence of any constraints however the number of parameters in each of the above three lnodels is exponential even the slot based model has NUM NUM parameters and thus it is infeasible to accurately estimate them in practice
in othc r words the preferred reading is that also NUM associates with b ul NUM and onlyj with paul i
we have found several divergences of this kind which seem to be stylistically motivated
this explains why 4f and 4f are both acceptable
we reported a simple semantic tagger which achieves NUM correct disambignation using two independent sources of information part of speech tags and dictionary definition overlap
approach 2a is the least promising since text tagged with word senses is practically non existent and is both time consuming and difficnlt to produce manually
tr3 obtains higher matching rates than the other two NUM which shows the effectiveness of the salience constraint in it
to get an overall agreement of greater than NUM NUM would require reducing the set of speakers from NUM to a carefully selected NUM
as shown in table NUM the increases of matching rates show the effectiveness of the constraint of discourse segment beginning in tr2
the overlap is the total number of times each word appears more than once in the dictionary definitions of all the senses in the configuration
tim av u age perplexil y of the qndcpcndettt slof nlodcls acquired bas d on NUM h assumpt ion t hal
the present study concerns this aspect of communication the nonnatural meaning in the restricted sense which is a core of intended communication
the situation seems clearer during lexiealization where a head noun may constrain an adjective hence the noun has to be generated first colloeational constraint
now what cot kl motivate the choice between the two remaining candidates to move u l to walk
when we get to the point of choosing a word it is the power of the language that drives us to say something that was not initially planned
there are several good reasons psychological and linguistic to believe that this sentence has not been planned from left to right and in one go
the hierarchical planning is realized by so called top down presentation operators that split the task of presenting a particular proof into subtasks of presenting subproofs
in sec NUM and NUM we describe the handling of paraphrases and aggregation rules two of the major tasks of our microplanner
an apo is mapped into an umo which is in turn expanded into a text structure by choosing an appropriate resource tree
as it will become clear when handling concrete aggregation rules such rules may narrow the realization choices of apos by imposing additional type restrictions
type checking during the construction of the text structure must ensure that the realization be compatible along both the ideational and the textual dimension
in the current implementation the rhetorical relation derive is only connected to one upper model concept derive a subconcept of cause relation
here is one of his examples the ship sank is an ideational event and it is textually presented from an event perspective
updating the global attentional structure these pcas either convey a partial plan for the forthcoming discourse or signal the end of a subproof
NUM NUM model NUM error correction
many cases sin e the hearer already knows the fact that the three components hold in the situs ion interpretation of irony results in confirmation of the mosl uncertain information that is the speaker s emotional attitude
however allusional pretense theory still sut ers dora the same disadvantage as other theories their notion of allusion is not clear enough and it does not focus on how hearers rec ogllize lltt erallt es t o be ironic
l arameters an be restricted i y infons for example t v l t r ix a parameter for tenlporal situations whml temporally succeed to
our theory agrees with this finding such kind of cues is only a l art of component NUM as we described in section NUM NUM and thus iron ul teranees without these cues can t e recognized as ironic
second they implicitly assume that the properties that characterize irony can be at plied to recognition of ironic utterances as they stand or they do not focus oil how hearers recoglfize utterances to rcb ironi
given two temporal loeatkms to and tt such that to temporally precedes t the utterante situation where an utterance is given is surrounded by ironic envir mmcnt if and only if it satisfies the following three conditions NUM
n parti ular an utteran e thanks a lot for exami le NUM is non ironic sar asln silt it does not allude to any exp etation
the slot focus is designed to hold that constituent
furthermore as we mentioned in section NUM nil ironic utterance achieves various olmnunieation goals held t y the sl eaker e.g. to be huinorous to enq hasize a i oint to clarify as i erlocutionary acts
the vert al complex is generated top down
this change an be rellected in the conceptual model by stating that tire l ij decisions are hla le ill increasing order of link length li jl and are no longer indepen lent
in a legal dependency l axse every word except for the head of the setrtence tile eos mark has pr words tags links words tags
these algorithms fall completely when faced with optional probabilistic rules such as flapping
tables NUM NUM show the percentage of raw tokens that were correctly tagged by each model as well as the proportion that were correctly attached to contage of tokens corrc0lly attached lo their paronl s by each model
but model c says that speakers primary goal is to flesh out the syn tactic and conceptual structure or each word they utter surrounding it with arguments modifiers and flmction words as appropriate
figure NUM bsf and deel cases
has a greater affinity f r fl ll than lbr llen e stock as w ll as price will en l tt t ointittg to the verl ell
per tagging baseline per ol lnance wa s i leaslli ed by assigniug each word ill the test set its most frequent tag i any roiii the trainlug set
again scores can be easily updated when spans combine and the probability of a complete parse p divided by the total probability of all parses that succeed in satisfying lexical preferences is just p s score
this section outlines the ostia algorithm to provide background for the modifications that follow
the algorithm begins by constructing a tree transducer which covers all the training samples
this is demonstrated for the word importance in figures NUM and NUM
the first cml to the procedure update in the main program takes time proportional to t
when i is retrieved rule ri is considered for application at each node n in rule i
case NUM if nt q then we know that s is the bottom most subtree in a p chain
the solution we propose here for critical rules is based on a preprocessing of the rule sequence of the system
let us assume that rule r is critical and that p is the only periodic node in q
the re implementation was programmed in c on a hewlett packard NUM NUM workstation running hp ux
pronunciation by analogy pba is an influential psychological model of the process of reading aloud
an input string is matched in turn against all orthographic entries in the lexicon
the output has been scored on words correct and also on symbol score i.e.
hence we believe it is sensible and important to test any pba system in this way
tim overall structure el pronouncf is as shown in fig NUM
if any product fell below a threshold during traversal its corresponding path was discarded
our best results are obtained with a scoring method based on a priori mapping probabilities
we will illustrate this in section NUM when discussing viterbi parses
definition NUM a b c d e
each state computes an additional probability its viterbi probability v
t viterbi parse i jy u
iterate these steps for all rhs occurrences of a null able nonterminal
computational linguistics volume NUM number NUM NUM NUM viterbi parses definition NUM
it is useful to translate consistency into process terms
each complete state derived by completion can potentially feed other completions
accomplish just that based on the notion of a wildcard state
algorithms for probabilistic cfgs can be broadly characterized along several dimensions
given a lig l vn vt vz pl s we want to find all the syntactic structures associated with an input string x NUM v
because the lig formalism is based on augmented rewriting the parsing algorithms can be much simpler to understand and easier to modify and no loss of generality is incurred
the cf backbone of a lig is the underlying cfg in which each production is a lig production where the stack part of each constituent has been deleted leaving only the non terminal part
we show that this cfg can be constructed in NUM n NUM time and that individual parses can be extracted in time linear with the size of the extracted tree
for a given a the number of triples b x r0 is the number of a productions hence o n
in a rightmost derivation at each step the rightmost non terminal say b is replaced by the right hand side rhs of a b production
the cf recognition problem for g x is equivalent to the existence of an s production in d x
therefore the static computation of a reduced ldg for the initial lig l and the corresponding c and
earley style top down prediction is used only to suggest worthwhile parses not to compute precise probabilities which they argue would be an inappropriate metric for natural language parsing
dealing with japanese to english translation besides german to english poses challenging problems
structures henceforth drss as its object language
the paper is organized in tile following way
its dialogue domain is restricted to appointment scheduling
multiple discourse relations on the sentential level in japanese
semantically it is an subordinate relation of explanation
the literature on earley based probabilistic parsers is sparse presumably because of the precedent set by the inside outside algorithm which is more naturally formulated as a bottom up algorithm
in section d the experimeld s are discussed and t he inethod is olnpare t with t hat proposed by kita et a l 199d
within this string that has already been extra ted as a candidate collocation there are two substrings that should e extracted and one that shouhl not
to make this lear consider the following strings new york stock exchange york stock new york and stock exchange
it will always be n a n b so whenever b is identified as a collocation a is too
we call the extracted strings candidate collocations rather than collocations since what we accet t as collo ations depends oil tile application
calculate their c value u NUM n a tbr all substrings b revise t b revise c b
where a is a word sequence la is the length of a n a is the number of occurrencies of a in the curpus
we make the above onditions more spe iti and give the measure for a string being a candidate coll cation
this second question turns out to be the actual focus of the article while the answer to the first question serves as a baseline
since dop1 only uses subtrees that are literally found in a training set it can not adequately parse or disambiguate sentences with unknown words
we used the initial random division of the atis corpus into a training set of NUM trees and a test set of NUM trees
coming back to our adjustment of the frequency of unseen np subtrees this can now be calculated by good turing as n1 no NUM NUM NUM x109
all of these utterances were available in text form NUM NUM of them were used for training with NUM NUM held out for test purposes
here the basic idea is that grammar rules tend in any specific domain to combine much more frequently in some ways than in others
the intent is to achieve a trainable robust parsing model which can return a useful partial analysis when no single global analysis is found
table NUM indicates that ebl and pruning each make processing about three times faster the combination of both gives a factor of about nine
frequency of co occurrence between word types u and v u n u v total number of co occurrences in the bitext frequency of links between word types u and v
b smith1 past2 spend his paychecka
the second sentence shifts the center to john
for our purposcs wc will ro ly
work are given in section l bur
in the second sentence the discourse center is o n c thus we get the reading in which jones saved jones tmyeheek as desired
our prime concern is to be able to exercise control over the mapping from the task model to the generated text
this center shift gives rise to the sloppy reading
important property of the competitive linking algorithm is that the ratio kiu n u v tends to be very high if u and v are mutual translations and quite low if they are not
the domain we have been investigating raises the particular challenge of mixed initiative dialogue the visitor is in control of macrolevel content selection but the system selects lower level content with the twin goals of satisfying the visitor s curiosity and of conveying key information
a number of nlg systems have now been developed to operate within a hypertext environment and now that these systems are becoming widely available on the world wide web it is useful to take stock of how well equipped nlg technology is to work in this new domain
our concern in this paper is to outline the most important of these indicating which have already been explored and which others deserve NUM a sample domain museum closer attention within the nlg community guided tours
because of the author s disappearance the effectiveness of the sampling is crucial authors strive to place links and important content in all the right places but some users still feel that they have to sample exhaustively every node of the hypertext
as a central objective of natural language generation nlg is on line creation of written document it is hardly surprising that a number of generation systems have been developed which use hypertext as their interface
other systems using more advanced nlg techniques allow the text to be customised in terms of both content and presentation form being sensitive to such factors as the user model characteristics of the user the discourse history a record of information presented so far and the system s goals what the system means to achieve
the system is designed to simulate the interaction of a museum tour guide and a browsing visitor this interaction is described in more detail in section NUM in section NUM the browsing task provides the context for a general discussion of the factors which influence the ability of a hypertext system to function dynamically and in the light of this discussion some observations about existing dynamic hypertext systems
we ran two curator of oz interviews where a curator of the modern jewellery gallery was v can we look at case number NUM now object number NUM this one here c yes you ve made a link with the first piece that we looked at which is the idea of a piece of jewellery which is also a work of art and a sculpture describes jewellery
the rather high reliability of even those nouns that are not statistically significant indicators of adjective sense suggests that in general text as well as in the co occurrence sentences most nouns are highly specific to the sense of their modifying adjectives
the critical noun is food but it is not a directly usable indicator light food may be either not heavy as in this sentence or not dark as in the previous example
finally in the case of short the vertical extent feature characterizing its not tall sense is appropriate to relatively freestanding entities that are normally vertical and humans are the most talked about instances of such objects generally
the point of this paper is to emphasize that although a particular swiss german dialect renders natural language syntax non context free it does not entail that natural languages induding the ones that license cross serial dependencies incur the worst case recognition complexity costs for indexed languages
in the first sentence something is not modified by hard at a deep syntactic level it is instead to speak something that relates directly to hard the surface modified noun being simply irrelevant see section NUM NUM NUM
we have not in oven the equivalence we conje tllre bel we tl our inetagranunatical method and the reduplication contex free grammars rcfc s that savi ch introduces as generative of simple rpda languages
the string duplication languages are not context free although they are closely related to the string reversal languages lcb wwr w NUM lcb a b rcb rcb where the r indicates the reversal operator which are context free
in the next case the noun is not intrinsically irrelevant but it turns out not to be useful pieces is virtually empty semantically and can be modified by the target adjective in either sense see section NUM NUM
together these features discriminate the target senses permitting a more compact and conceptual rather than word specific representation of the indicators section NUM NUM about three quarters of the adjective instances are disambiguated by these features and virtually errorlessly
so that inputs like NUM NUM mill arden dollar zweiundzwanzig dollar or dreiundvierzig mill arden dollar are automatically recognized
null in the default case it this this kind of information which is the input to the th ls component text handling to linguistic structure of the system
this is important as some of the phenomena we were attempting to match were complex see below and occurred in a vm iety of formats
the definition of such generic entries in the lexicon keeps the lexicon smaller by dealing with what otherwise couhl only be coded with an infinite number of entries
some specialized features are also provided for the tagged words allowing to characterize them more precisely so for exmnple acr0 for acronyms and so on
then yasuda vs genii yasuda one can never be sure that an english word is not a name in another language
the algorithm was run on the entire bnc i.e. each of its NUM NUM files
a rectangle object which hides the specified circle object on the screen
expresses tf e othellts rcb y itself
a simple example shows how difficult it is to understa nd
oi ly i he first mouse inlmt is iuterpreted
ha re a otle t o olle eorresl olldell e
a drawing with a light pets and some typed comttta nds
fol nnill i lilodaj systems mull i moda l
i efinil e cla use 71rajllilia l
in particular we found that simple verb forms usually suffice as extraction patterns in the terrorism domain e.g. x was killed
in the first experiment we applied autoslog ts to NUM texts 1deg from the muc NUM corpus which has been preclassified for the domain of latin american terrorism
the autoslog ts dictionary achieved higher recall up to NUM which makes sense considering that the autoslog ts dictionary is much bigger than the hand crafted dictionary
if the targeted noun phrase is the subject or direct object of a clause then autoslog infers that the verb defines the role of the noun phrase
circus relies entirely on its dictionary of concept nodes to extract information so it is crucial to have a good concept node dictionary for a domain
pattern NUM is the only one that is satisfied so a single concept node is generated that recognizes the pattern x was bombed
thresholds can be set such that some of the lower frequency n grams are discarded
these results also provide insight into the relationship between homogeneity and language model quality
the human interpreted setting had the highest rate of accommodation
in the human interpreted setting there was no difference
computers were used only in the multimedia condition
on the other hand there is no other external evidence available
finally we draw implications for the design of multimedia human computer interfaces
subjects could not see one another or the interpreter wizards
this tendency resulted in the highest level of accommodation in the human interpreted setting
empirical investigation is required to determine if an optimal balance can be reached
we call such a fact an elementary event ee hereafter
each algorithm is a filter containing several interpretation rules ir
we have proposed a methodology to resolve anaphors occurring in embedded sentences
NUM john walked into the room
in fact only previous sentence entities are present in the focus registers
thus phrases of the current sentence can not be proposed as antecedents
NUM john s father s portrait of him
the sentence NUM should require specific treatment though
count when applying the focusing algorithm
details of an evaluation are given
if the translation in the bitext is consistent and the model is accurate then a should be near NUM and ashould be near NUM
a cooperative speaker uses dense words NUM or technical terms computer space shuttle only for people whose lexical competency allows them to understand their meaning otherwise he will decompose these words
if my line of reasoning concerning message planning is correct namely that planning is basi ally a two step process where l irst a skeleton is planned general plan and then its constituents specific plan then this should have consequences on the overall architecture o1 generators as well as on the infornmtion flow control process
while the second cmldidate 1o swim is simply in contradiction with tmrt of thc initial specification location ground the last one to run expresses more than the initial message phnmcd
during the first deep generation conceptual choices are made content determination discourse planning during the second surface generation linguistic operations are performed word choice determination of syntactic structure
first of all the order of thought i.e. the order in which conceptual chunks become available and the utterance order i.e. the order in which words have to be uttered in a given language is not necessarily the same
planning proceeds thus from general to specific breadth first that is sentences are planned incrementally by gradual refinement of some abstract thought rather than in one go one shot process where every element is planned down to its last details
choosing to discard NUM q3 q9 the pruned automaton for the example lexical entry looks as displayed in figure NUM NUM note that word class specialization of lexical rule interaction does not influence the representation of the lexical rules themselves
the tokenizer separates punctuation from words
next the delimited names are categorized
three experiments were conducted for spanish
some of this knowledge is constant across languages
a synopsis of learning to recognize names across languages
the same three experiments are being conducted for japanese
locations by contrast exhibited the lowest performance
brandon to be a person name
the genotype concept allows generalizations to be made across words according to tag patterns thereby gathering estimates not on words but on tag occurrences
however this is not necessarily true of sparse grammars
this reachability criterion has to be extended in the presence of null productions
since the grammar has only a single nonterminal the left corner matrix pl has
these are exactly the paths summarized by the inner probability NUM
the value NUM is just a special case of the definition
where s is the sentence nonterminal note the empty left hand side
to complete the description we need only specify the initial and final states
scanning ensures that the terminals produced in a derivation match the input string
known specialized algorithms but can be substantially better on well known grammar classes
the probabilities of interest mentioned in section NUM can now be defined formally
the other is the type of verbs which are used only for volitional actions
therefore we decided to pay more attention to a different paradigm which captures more information about the word at a morphological and syntactic level
table NUM an example of cost computation for the bigram fst p r jmp nmp
statistical decisions on genotypes are represented by weights the lower cost the higher the chance of a particular tag to be picked
NUM from the baseform of the word one could estimate the frequency of the analyzed stem in the process of morphological analysis
this is especially helpful for automatically generated lexicon entries such as nominalisations which are created from verbal entries using lexical rules
let us now consider some of the entries for the spanish verb adquirir with the following corresponding semantics acquire learn displayed in figure NUM
we have developed a suite of tools to help in testing the analysis lexicon to ensure the high quality of our large scale lexicon
tliis implies having access to a conceptual lexicon which will serve as a pivot point between the analysis and the generation lexicons
the process is not perfect some information required in generation such as collocational constraints is not typically recorded in analysis lexicons
null acquiring a large scale lexicon is very expensive which is why building lexicons that are reusable for other domains or applications is recommended
moreover it can handle phenomena which are out of the reach of other approaches and yet are necessary to enhance lexical choice in generation
but in practice this approach complicates the process of lexical disambiguation for parsing and lexical choice in generation by an unjustified proliferation of entries
and finally theoretical issues concerning the content of analysis lexicons generation lexicons and the text meaning representation language can be more fully investigated
basically when the centering algorithm is used for the zero pronoun resolution the algorithm first generates all possible antecedents for zero pronouns in a sentence by enumerating all possible ct and c pairs for the sentence and then filters and ranks these possible antecedents with the constraint and rules that are mentioned above
hi spite of l h se ndv mt gcs unt ortunat ly the usefithmss of tim previous centering flmneworks has not flllly tc stcd lmcmlsc only a small number of construcl ed discourses have b cn usc d for cv fluation
to clarify how tile zero pronoun resolution relies on the intormation of conjunctive i ostpositions we pertorin the investigation whether tile noun phrases with the same grammatical property agree in two adjacent simple sentences that have a conjunctive postposition tmtween timm by extracting sentences with conjunctive postpositions dora the revie w articles in the newspaper and enmnerating the agreement and disagreement
since the antecedent tends to appear in the closer sentence to he zero t ronoun as 141jisawa s investigation indicates we deternfine the following forward center ranking among the cfs of tile previous four simple selltences c rcb c rcb where c rcb represents the c i of the n th silnple sentence froln the current sentence
since conjunctive postpositions of class a have a strong preference that two subjects in adjacent sentences tend to coincide instead of the centering alger thin we use this preference tbr tile zero pronoun resolution in the simple sentence after the conjuimtive postpositions of class a and try to find the antecedents of zero pronouns in the same position of the adjacent sentence if any
the important point for multilingual generation is that the absence of a domain specific verb in one language does not affect lexicalisation in the other one i.e. a specific verb will be used if available NUM the second type of differences is a more complex issue
the role of the text generator is to propose bilingual drafts of procedural texts intended to be integrated in maintenance manuals and to perform rephrasing operations which may be requested by the technical author for example grouping maintenance instructions at surface level or changing the specificity level of an instruction
technical documentation appears as a promising application area for text generation several works NUM NUM NUM NUM NUM l demonstrate that nlg techniques may contribute in the future to make technical documentation more reliable and maintainable
the multilingual generation approach stipulates that technical documents such as maintenance manuals can be generated automatically in several this paper partly covers a work made by the au null languages from knowledge bases used in design processes or constructed for the purpose of automatic documentation production
the english equivalent of 1f found in the corpus is based on the verb fill which takes as direct object the translation of the argument of the predicative noun remplissage in 1f 1e fill the hydraulic reservoir
for exampie in sentence 1f the operator verb procgder takes as its direct object the predicative noun remplissage which in some way denotes the action to be performed 1f procdder au remplissage du rdservoir hydraulique
we will focus on three types of lexical divergences which are frequent in the analyzed procedures null NUM domain speclfic vs ordinary verb the two verbs have similar argument structures but one of them belongs to the technical jargon of the domain
lle seems more specific for the unlocking action though incompletely specified by the main verb remove is somewhat suggested by the argument loekwire since obviously the function of a lockwire is to lock
in this t aper isamap NUM a hand crafted japanese thesaurus is used as a ore
n this l aimr the followi g three hytmtheses a re used to resolw l he probhmt
the feature bundles constitute the main datastructure of the t l atself
a if synonym in levin select the class that has the closest match with canonical ldoce codes
semant ically each pair of a role and the predicate directly dominating an elementary argument demands particular selectional features for that argument
i ltm e work will concentrate on evaluating the benefits of this approach fl r eomtml ational text analysis
the parameter state is filled in by disp own which is added to the predicate hierarchy sketched already as a specialization of the predicate iiavf
it consists of a predicate and a nmnber of arguments each of which is either a predicate argument structure or an elementary argunlent
i l source have ec st u from obj have cc e dc sl
by adding infbrmation about emphasis and blocking of roles a bsf is transformed into a number of prototypieal meaning descriptions
calvin giw s as a NUM resenl a book to hobbes 2b calvin qlaubt
on the other hand they an be used for a detailed reconstruction of the inferences inentioned in section NUM
the grammatical realization of the optional actant an accusative is put in brackets z grammatical case assignments
section NUM NUM generator and the contextual disambiguator cf
for this reason we felt it necessary to determine the importance of negative evidence for building uniquely identifying syntactic signatures
the second three way distinction involves prepositions and breaks the two previous distinctions involving negative evidence into three sub cases
de pintelaan NUM 5k3 b NUM gent belgium peter
in general trafficking and crime need not be similar of course
the asterisk marks the plot of the success rate for the narcotic sense
table NUM a summary of the experimental results on four polysemous words
the average success rate of our algorithm was NUM
the method is based on word similarity and context similarity measures
figure NUM shows how the similarity values develop with iteration number
also note that the similarity is affected by the training corpus
first we replace the an or none indicator of cooccurrence by a graded measure of contextual similarity
the paper ends with a discussion section NUM
this method is applicable to rather small unaligned corpora it can extract correspondences between compound words as well as simple words
these are major causes of errors in word correspondence extraction refining the nominal compound extraction procedure will considerably improve recall and precision
the experiment confirmed that the proposed method can extract not only compound word correspondences but also simple word correspondences from a small corpus
the target of extraction can usually be restricted to the correspondences between content words which are characterized by both dominance in number and straightforwardness
according to utterance plan r17 this model can start the following utterances to satisfy the time constraints before obtaining a concrete domain plan such as r3
the essence of the method is to calculate correlations between words based on their co occurrence information with the assistance of a basic word bilingual dictionary
if we calculate the pairwise correlations between the contexts in which the words occur a correponding pair of words will show a high correlation
first most of theln assume that the input corpora m e aligned sentence by sentence which reduces their applicability remarkably
the method risks introducing search errors but in practice efficiency can be greatly improved with virtually no loss of accuracy
for each span of words in the sentence the probability ph of the highest probability constituent is recorded
ideally we would like to integrate pos tagging into the parsing model rather than treating it as a separate stage
the head words of the two nps resignation and yesterday both modify the head word of the vbd announced
these rules are also used to find the head word of basenps enabling the mapping from s and b to s
this paper describes a new statistical parser which is based on probabilities of dependencies between head words in the parse tree
these figures count basenps as a single word and are taken from wsj verbs between the head words involved
where y is the set of all words seen in training data the other definitions of c follow similarly
these a vmues have the desired property of increasing as the denominator of the more specific estimator increases
the parser finds the tree which maximises NUM subject to the hard constraint that dependencies can not cross
the latter two instances of of are so distant from sales that it is unlikely that there will be a dependency
the morphological recogniser tries to identify the unknown form by computing its potential linguistic characteristics including its canonical form
the numbea of paths found depends heavily on the richness of the lnodels used which varies with the types involved l r instance the model for type angioplasty involved in table NUM is central in the domain
where predicate began expects an event as its second argument so that some way must be found to relate the object novel to an event such as to read a novel or to write a novel
it is the most eoinplex in the knowledge base and ontain8 NUM oneet ts and NUM relations which at counts fl r the greater number of paths found in these examples
in addition we deliberately left some ambiguities pending for the syntactic parser to avoid the danger of overkill el
in the current state the ontology contains about NUM NUM types and NUM relation types over NUM types have their own reference model the lexicon defines over NUM NUM predicates and about NUM grammatical relations and prepositions
actually a difference is made between the regular full form database dictionary and a much smaller canonical form dictionary
at a later stage however jeida sentences usually gave several translations and lexicalization with careful assignment of weights was the most critical task
4unlike in english the past participle in dutch does not need to occupy a position adjacent to the auxiliary
consider the following proof that xo y yo z xo z
fhis is easily a hiev xl
where two formula are combined their contexts are merged and must be consistent
figure the coml iladon pro edure
for iml li adonal fornmla how
i et us turn now to the general case where higher order formulae are allowed
NUM the set of formulae arises by closing a nonempty set of atomic types NUM
tpb total parse brackets number of brackets produced by the parsing system
in other words how often do arbitrary choices influence the result
this is particularly useful if the parsing system was made independently from the treebank
an i pda is just a pda which has a special type of symbol thai can tie put onto the stack to nlake the machine treat the part of the stack above it ms if it were a queue
the implementation described here handles a variety of word level messy details efficiently speeding up overall processing time and simplifying the grammars and lexica
the komet grammar of german NUM NUM is a computational nigel style systemic functional grammar based on the notion of choice
a number of discourse model based speech generation systems have been proposed that address exactly this problem for example newspeak NUM NUM
intonation is not only used to mark sentence internal information structures but additionally it can be employed in the management of the communicative demands of interaction partners
deciding between declarative and interrogative as realization of a move request requires information about the immediate context of the utterance i.e. about the dialogue history
we are aware that we have left untouched a number of NUM problems that are involved in the generation of appropriate intonations
in this article we have developed a model for guiding the selection of intonation in a system supporting human machine interaction in retrieval dialogues with spoken output
inform the system answers the user s question i.e. it is giving information and hence the speech function is statement
in order to do llin the analysis d sub dialogue r r r request i i inform
tonality the distribution into tone groups i.e. the number of tone groups allocated by the speaker to a given stretch of language
again the relation between speech function and mood is potentially many to many all of imperative declaralive and interrogative may for instance encode a command
an event including an activity is a culmination as an example consider the event of oil draining from an engine which is given here in an abbreviated kl one notation roles names in capital letters instance names in lower case
note that our ducative causative extension rule given fact has critical influence on the holistic interpretation of mass flollns above applies in this case and extends the coverage of the sitspec to one corresponding to tom drained the water from the tank
some verbs as is well known can occur with either a path tom walked into the garden or with a place tom walked in the garden and only in tile garden can here be treated as a circumstance
the primary contribution of research reported in this paper is that we ha ve proposed a method of learning dependencies between case fi ame slots which is theoretically somld and elficient thus NUM roviding au effective tool for acquiriug as depend racy information
the next fornmla adds the constraint influences grouped according to the variables they involve then multiplies the results of each group to get the final value NUM NUM NUM the last formula is tile same than the previous one but instead of adding the constraint influences in the same group just picks tile maximum
using this new supt ort fun tion we obtain resuits slightly below those of the iimm tagger our sut i ort fun tion is tim sequence NUM robal ility which is what viterbi maxinfizes NUM ut we get worse results
w prefer tll use of supervis d training sin e large enough corpora arc available because of the difficulty of using an unsut ervised method such as bmm welch re estimation when dealing as in our case with heterogeneous constraints
this is becmme the training cortms for wsj is much bigger than in the other cases and so the trigrmn model obtained is good while for the tiler c rpora the training set seems to t e too small to provide a good trigram iniormation
that is we are establishing two classes of constraints the autoinatically acquired and the mmmally written
the aim of the algorithm is to find a weighted labeling such that global consistency is maximized
corpus sus susanne train 141kw test 6kw tag set size NUM
the constraints do not intend to be a general language model they cover only some common error cases
our experiments will consist of tagging a corpus with all logical combinations of the following parameters support function updating function compatibility values normalization function and constraints degree which can be binary ternary or hand written constraints we will experiment with any combination of them as well as with a particular combination consisting of a back off technique described below
h b0 b corresponds to literals in prolog
the phase NUM parsing onsists of the folh wing steps
res eliminates the feature structure nodes which is specified by a restriction schema
c what information is available at the onset o1 lexicalization all or only part i.e. at what moment is the word s underlying meaning fully specified
probabilistic parsing of unrestricted english text with a highly detailed grammar
data names of the variables nnee compagnie profits depenses types of the variables with aux
NUM important factors in the generation process a number of factors have to be considered in order to produce a statistical report containing text and graphics
figure NUM schema colonnes3 comparison of the profit average and the spending average of the companies NUM
for example knowing that may and july are months allows a generator to produce temporal expressions such as two months later NUM
the system is given the data in tabular form as might be found in a spreadsheet also input is a declaration of the types of values in the columns of the table
the output was generated by the system but the information was manually re ordered and formatted in order to better satisfy the space requirements of this article
in particular the graphs are presented at roughly NUM of their actual size and the structure of the report was flattened by removing section titles
since evolution is one of profits the most frequent goals in a statistical report the temporal knowledge built into pr texte proved very useful
this system was well suited to our needs for two reasons first it was developed in prolog making it easy to integrate into post graphe
this reordering has two positive effects it further destroys the impression of evolution by making the years nonsequential and it allows a better comparison of the profits NUM
the text is also different from the one in figure NUM instead of describing how the profits evolved it merely points out the best and annde the worst years for profits
any of this information is available for a selected node
there is of course no reason why the best head driven statistical model of a given language must coincide with a grammar derived by a linguist
thus we have a representation which has factored out many details of phrase structure that are unimportant as far as minimizing entropy is concerned
finally since what would have been multiple parse hypotheses are now one a viterbi learning scheme is more likely to estimate accurate counts
the parser is trained and evaluated with the spontaneous scheduling task which is a negotiation situation in which two subjects have to decide on time and place for a meeting
further when comparing the sentence with its feature structure it appears that there is a correspondence between fl agments of the feature structure and specific ctmnks of the sentence
NUM he training set consists of NUM sentences the devejopment test set of NUM sentences and the unseen evaluation set of NUM sentences
when building complete feature structures these network errors multiply up resulting in not only that many feature structures are erroneous but also inconsistent and making no sense
monday i assume you mean monday the twenty seventh null of feature pairs with atomic wdues make up tile braimhes and the ln anches are connected with relations
it is indicated by and means that the neural network only detects the occurrence of a value whereas the value itself is found by a lexicon lookup
due to the ability to model relations of more than length NUM no nesting depth problems occurred while modeling over NUM sentences from the english spontaneous scheduling task esst
manually aligniug the sentence with fragments of the feature structure gives a structure as shown in figure NUM a few coinments apply to this figure the sentence is hierarchically split into chunks
as the neural networks learn their tasks based on the microfeatures and not based on distinct words adding new words using the same microfeatures is easy and does not degrade general null ization performance
the subcategorization is shared between head dtr and mother
subcat is inherited from the head dtr
t went y t o ninety etc card ee inl two to ten und twenty to ninety i
then the ascii text first goes through sgml based tagging convertion to an edif eurotra document interchange format format then paragraph recognition sentence recognition and word recognition
null another major result of the corpus investigation was that most sentences coutmn so called messy details brackets figures dates proper names appositions
structure of vorfeld and nachfeld nps pps aps cardps coordinate structures occurrence of expletives pronominals and negation occurring in the corpus was made which then guided grammar development
rood oo et l sudca func projects npacc NUM is the entry for in as a head of a pp subcategorizing for an npacc
NUM represents the treatment of e i umlautung as occurring in german verbs like gebe gibst referring to trostg0
refinement mentioned al null ready is a monotonic appfication of phrase structure rules and lexical entries to further featurize flesh out with features a finguis6c structure established in analysis
no local collocation knowledge is used
NUM NUM evaluation on a common data set
table NUM distribution of sense tags
we first set aside two subsets for testing
table NUM comparison with previous results
table NUM relative contribution of knowledge
the complex expression lcb a b b c ii x y rcb NUM which contains multiple replacement in one left and right context can be written in a more elementary way as two parallel replacements
l igure NUM the ppi r encoded as a definite relation
lexical rules henceibrth lrs have been subjected to particularly close s rutiny
comps takes a list of categories ailed synsem objects as its vahn
lowever since in german only main verbs can be passivized the sentence is ungrammatical
l fhis paper will not provide a formal definition of subsumption or nnificiation for typed feature structures
he original includes the feature valence abbreviated as val in lqg
the set of base lexical entries and the set of lexical rules specified by the grammar
NUM l s n kiinncn wird r cs
contained in the conq i nt specifications of the nps
after initialization the deterministic parser applies a sequence of parse actions to the parse structure
during the transfer process the resulting parse tree pairs are then accessed using pattern matching
table NUM compares our results with spatter and bld
slots include special information like the numerical value of number words
for our parsing test series we use NUM fold crossvalidation
however such feature set additions require fairly little supervisor effort
figure NUM example of a parse tree simplified
as a control half of the evaluators were also given translations by a bilingual human
our parse action sequences are too complex to be derived from a treebank like penn s
and obviously the existence of multiple correct taggings for a word is to be expected a fortiori where a highly ramified system of semantic categories is involved
llps lends il self best to heiul driven t otj olu u l i roc essiugl
are specified as one large eatm e descril cion containing ac h asc one dis junction
fable NUM processed underlying punctuation rule p tterns
several studies have already shown the potential for using punctuation within nlp
NUM and more than half of these related to the comma
a suitable methodology for applying tmnctuation to existing grammars has also been suggested
ill they said we went to the party
it unlikely to be used more fl equently in any other circumstances
the only real underlying patterns are those in table NUM
there appear to be two general rules which overlap slightly
thus the generalisation in NUM is the most appropriate
itence some further theoretical work seems to be required to constrain the applicability of these rules
but if on the other hand the dictionary distinguished stake as an investment and stake as an initial payment in a game or race then the answer would be expected to be different
distance from the beginning of a text in general sentences located near the beginning of a text tend to be important
luted by multiple regression analysis is more similar to the human selection than the weight set NUM created according to the author s intuition
those in group a selected important sentences about NUM NUM of the article in NUM editorials and NUM general articles from the nikkei newspaper
therefore sentences in the last paragraph are given NUM points for this feature sentences in the previous paragraph NUM and so on
rhetorical relation the rhetorical relations to the preceding context is analyzed as example adverse parallel comparison or connection
creating an abstract requires deep semantic processing with broad knowledge and the strategy for generating an abstract depends on the type of target text
therefore sentences in the first paragraph are given NUM points for this feature sentences in the next paragraph NUM and so on
just as the word bank can be assigned different senses in different contexts so can boeing NUM jet be once a product and another time an equipment and not a product depending upon the context
in this paper we describe a method of creating spotters for entities of a specified category given only initial seed examples and using an unsupervised learning t rocess to discover rules for finding more instances of the eoncet t
in this paper we presented the universal spotter a system that learns to spot in text references to instances of a given semantic class people organizations products equipment tools to nmne just a few
for example people nmmes m e usually sequences of i rot er nouns while equipment nmmes rare contained within noun phrmses e.g. forwmrd looking int m ed radar
we use NUM art of speech information to delinemte those se lllelt es of lexicml l okens t hat arc likely to ont mill olll enl itics
the seed should identit y the sought after entities with a high precision thougil not necessarily NUM however its recall is assumed to be low or else we would already have a good spotter
in the silnplest instance of this process we consider a context to coilsist of n words to the left of the seed and n words to the right of tile seed as well as the words ill the seed itself
a specific class spotter is created through an unsupervised learning process on a text corpus given only an initial nser supplied seed either a number of examples of the concept or a typical context in which they can be found
we used the universal st ot ter to find organizations an lcb NUM products in a NUM mbytes cortms consisting of al ti les fl om i ll wall street journal
our method illoves t eytmd the traditional name si otters and towards a universal spotter where the requirements on what to spot can be specified as input paraineters and a specific purpose spotter c ouht be generated automatically
the symbols c dn n tn and t n etc are abbreviations for bundles of features
after the retrieval of all the words in a sentence a set of lexical rules are matched against the lexical items
the developmen t prior to august NUM NUM was mostly on general tools that can also be used in non muc applications
for example to modify the features of a class of words on e needs only to modify the abbreviation file
in spite of the radical difference between the high level approaches of our muc NUM and muc NUM systems over NUM of the code of the muc NUM system was reused
given an input sentence such as NUM principar returns either a constituency tree figure 2a or a dependency tree figure 2b
the status field in the records created by the rule static is usually unk unless the post has modifiers such as former or retired
the pattern matcher searches the dependency tree rooted at the person and find a post or posts and an organization skipping all the nodes that are also persons
the te and st modules are trained on less than NUM of the NUM formal training articles though they are tested with the complete set durin g development
james status unk post chief operating officer org holder john j dooner jr status unk post president org holder john j
figure NUM table of backtrack points b2 is encountered outside of the ego of bt
in the second compilation step section NUM NUM we determine the possible interaction of the lexical rules
rules NUM and NUM as possible followers of that sequence since their in specifications do not unify with those values
NUM note that an automaton can be made even more deterministic by unfurling instances of cycles prior to pruning
instead the disjunctive possibilities introduced by the frame specification are attached as a constraint to a lexical rule
the predicate argument structure is reflected by corresponding features args contains a list of arguments
NUM we use rather abstract lexical rules in the examples to be able to focus on the relevant aspects
clearly features instantiated for some ego may have effects onto the pre or post context
intuitively understood unfolding comprises the evaluation of a particular literal in the body of a clause at compile time
a comparison of space efficiency between an expanded out and a covariation lexicon needs to compare two different encodings
in this case they must allow for the the application of the same subset of criteria
for instance lexical choice and constituent order constraints may suggest the use of passive voice cf
the properties are organised as a multiple inheritance graph divided into a number of sub graphs each corresponding to a specific feature NUM
so a global solution which maximizes the utility from the entire game may maximize the utility from some constituent games but not from others
NUM if an element of cf ui j is realized by a pro null noun in ui then so is cb u lcb
to illustrate a meaning game and to demonstrate that meaning games are not cheap talk games let us consider the following discourse
sul pose for examl ile that in the analysis of the setltellce l saw a girl with a t elescope NUM two interpretatiolls are obtained
in the test data which are of verb prep t nount prep nou pattern there were NUM examples that involw
thus the target joint distribution is likely to be a pproximabie by the product of several component distributions of low order and thus have in fact a reasonably small number of parameters
is given a specilic l robability by a class based nodel where l e son alid airplane denote word classes
consider the ungrammatical double coordination example in NUM
most simply there is the size of the training corpus a larger training corpus means a smaller loss of coverage due to grammar specialization
thanks to an anonymous referee for valuable comments and to the silc group members xuanyin xia eva wai man fong cindy ng hong sing wong and daniel ka leung chan
e s t denotes the set of english words that are translations of any of the chinese words anywhere within c t
in other words the chinese can not be appropriately segmented except with respect to the target language of translation a task driven definition of correct segmentation
with a sbtg the second possibility is to use a stochastic bracketing transduction grammar sbtg in the channel model replacing the translation model altogether
note also that we assume the explicit sentence start and sentenceend tokens co s and ct l s which makes the algorithm description more parsimonious
NUM reconstruction initialize by setting the root of the parse tree to q0 NUM t NUM s s
at present we are unable to use direct measurement to compare the speed of the systems meaningfully because of vast implementational differences between the systems
the lexical rut s zone specifies the morpho semantic rule which was applied to produce this new entry and the verb it has been applied to
ii a second predicative occurrence here c8 all instance of a template structured around a different predicate e.g. produce in fig NUM and which is used to express the intended result component
the left hand side antecedent par0 is always a syntactic condition expressed as a tree like structure which must be unified with the results of tile general parse tree produced by the syntactic specialist of the translation system
with respect now to the nl nkrl translation procedures they are based oil file well known principle of locating within the original texts the syntactic and semantic indexes which can evoke the conceptual structures used to represent these texts
binding structures i.e. lists where the elements are conceptual labels c3 and c5 in fig NUM ne second order structures used to represeut the logicosemantic links which can exist between predicative templates or teem fences
we can remark that all the details of the full template are not actually stored in the consequent given that the h temp hierarchy is part of the common shared data stmctmes used by the translator
in fig NUM cl and c2 are symbolic labels of occurrences move and produce are predicates sub j obj topic l propos of are roles
with the term nan ative documents we denote here nl texts of an industrial and economic interest con esponding e.g. to news stories corporate documents normative texts intelligence messages etc
language aims to propose some possible pragmatic solutions for the set up of a standardised description of the semantic contents in short the meaning of natural language nl n utative documents
the descriptive component concerns the tools used to produce the formal representations called predicative templates of general classes of narrative events like moving a generic object formulate a need be present somewhere
null cluster based in the case of the category based approach each noun in the training data was categorized into the leaf clusters of the bgh tree that is the NUM digit class categories NUM for the cluster based approach the nouns were also categorized into the intermediate class categories that is the NUM to NUM digit class categories
more precisely the difference between the probability of each cluster and the highest probability value for that level was calculated and clusters for which the difference was within a certain threshold were left as candidate paths
we feel that it is necessary to devise a new method that unifies the above two approaches i.e. to implement psycholinguistic principles of disambiguation on the basis of a probabilistic methodology
we first apply cfg rule l h m to h and m yielding category l see figure l a
many such attachments may be observed in the training data and we can formulate the frequencies of attachments NUM as a syntactic preference
lpr rap and alpp are known to be effective for disambiguation and these are the ones whose implementation we consider in the present paper
however as data size increases new words may appear and the number of parameters that need to be estimated may increase as well
the attachment sites would be determined to be the same however if we were to use two word probabilities c f resnik
we have employed two additional sources of information root word usage statistics and contextual statistics
having defined a lexical likelihood based on lpr and a syntactic likelihood based on rap and alpp we may next consider how to combine the two kinds of likelihood in disambiguation
in this paper we presented a simple method to handle complex sentences with the centering theory and described our framework that can identify the antecedents of zero pronouns in naturally occurring japanese discourses
a data set was constructed by applying the t insertion rule in NUM the t deletion rule in NUM and the flapping rule already seen in NUM one after another
as our test discourses we use NUM prepartitioned sentences from tive discourses in total whietl are a review article in the newspaper a tblk tale and a novel
lul h rlnore taking into a ouil lcb NUM he illformation of conjunctive t ostt ositions improve s
our algorithm guesses the most probable phoneme to phoneme alignment between the input and output strings and uses this information to distribute the output symbols among the arcs of the initial tree transducer
while it might be unreasonable to expect any transducer trained on three samples to be perfect the transducer of figure NUM illustrates on a small scale how the ostia algorithm might be improved
rules for each sentence ui NUM
when walking down branches of the tree to add a new input output sample the longest common prefix n of the sample s unused output and the output of each arc is calculated
here NUM represents a zero pronoun
figure NUM subsequential transducer for english flap
figure NUM three rule transducer induced from NUM NUM
thus a noun and a verb which have similar senses are assigned similar classes in the thesanrus
the results of a small experiment are presented and the proposed measure is evaluated section NUM
it is necessary to further merge the clusters so that exactly one cluster corresponds to one hand classified sense
however these clusters are cotrect in that none of them contains examples of more than one hand classified senses of kau
it according to the likelihood that a member of the class will actually appear as the object of the verb
the basic idea presented here is to distribute the labor of evaluating the constraints in the grammar on several processors i.e. parsers
if at least one such constraint is a filtering constrmnt we automatically enlarge the language accepted NUM y this sul grainmar w r t
we will present examples in which sentences with lower structural complexities are easier to process than similar sentences with higher structural comph xities
definition NUM NUM struetural complexity the slructural complexity of a dependency struclure is the total length of the dependency links in the structure
similar approaches especially for the syntax semantics interface have been suggested for all major kinds of unification based theories such as lfg or cug
icnerally spealdng pps contain more wor ls than nps and cl s con rain more words than l ps
obviously the major advantage of our method ix that unification and copying l ecome faster during processing due to smaller structures
the column syn shows that parsing with syntax only takes NUM of the time of parsing with the complete grammar synsem
l he notion of structural complexity provides an explanation of the difficulty of processing center embedding that makes much weaker commitment to the parsing model
for example compare the following to sentences NUM a fhe cat that the dog that the man bought chased died
the content by adding or deleting some inlbrnmtion or carry over to the next cycle word or sentence the specific parl that couldn l be expressed cany over phenomenon
if conciseness is what we are looking for then the most specific word to walk is to be preterred to the more general term to move
the task of the rule shown in figure NUM NUM is to completely disambiguate the representation of the destination of a path
when choosing words we express not only a given meaning but we may end up adding to the conceptual structure message meanings thin initially we had not planned
my comments under the figures should be read in the following way underlined element currently obtained result capital letters variable hence not yet fully specified element
the point NUM am trying to make is that people probably start by planning things globally filling this plan with details at a later stage local planning
as i will show by taking an example from the lexicon both these conclusions are ill lbunded as they suggest that there is no feedback between the different components
while there are good reasons to believe that at an early stage of processing the tmderlying meaning of words is underspecified rod NUM dense words are generally abstract words like inflationrate superstition belief
this could yield something like man attribute meaning that the person who sees the event is a man a terminal element whom we want to describe further by providing a elmmcterizing attribute
while this sounds self evident it contains at least two assumptions that are easily overlooked a thought precedes language b thought is entirely encoded or specified before lexicalization takes place
for example the generation of the first word of the sentence here above tile temporal adverbial when requires knowledge of the fact that there is another event taking place
after termination all feature structures in the blackboard are marked so as to prevent re execution of an already applied rule
each structure represents the semantics of a phrase of one of the main syntactic categories np vp or pp
after the first call to procedure update we have
the hou analysis u r l l oposal is to analyze s es as involving a
intuitively the only property of the form likeing y that holds of jon is the property of like ing mary
focus operators qttantifieational adverbs is made contingent on the fsv which itself wtries with the choice of focus
NUM unification yields another possible value of c d namely a yxx l x m
after the first question has been answered the semantic level looks like the one shown in figure NUM
information in state is used to detect these cases
third and perhaps the most important in our system the linguistic and statistical estimations are entirely done on the genotypes only regardless of the words
we have considered a typical ambiguous genotype imp nmp which occurs NUM times in the training corpus almost evenly distributed between the two alternative
in order to prefer trigrams over bigrams and bigrams over unigrams we have added a biased cost to some transitions
as an example we extracted the most frequent open class genotypes from the training corpus each of them occurring more than NUM times shown in table NUM
in our work the entire algorithm bases estimations on genotype only filtering down the ambiguities and resolving them with statistics
this prevents disambiguating inferences for cases where there is no choice with respect to the interpretation of tile discourse sister has to be interpreted as sister although there is a contradiction
this restriction prevents the attempt to prove the inconsistency of the discourse alone at least if mp does not contain logically valid subsets of formulas that we assume and are able to decide
in order to disambiguate properly we have t6 consider only those consistent sets of information pieces which contain at least one occurrence of the predicate that represents one reading of t he ambiguous lexical item
let us take for example the set of clauses obtained fi om the scfs of the memfiilg postulate NUM aim the discourse NUM by the standard preparation pro edures
without any flu ther restrictions on tile expressive power of NUM he representation language and or the underlying logic the inconsistency of the representation of an arbitrary text and our conceptual knowledge is not decidable
whether we need this entire set or just a rather limited subset of pieces which can be made accessible by locally restricted conversions into scf is for a first order discourse an empirical trot no formal problem
hence our procedure already reflects in a very weak sense the discourse structure since we did not allow all conversions preserving logical equivalence but only those needed to construct an scf froln the discourse
this set was crucially dependent on what is said and not on what follows since we were especially in case of inconsistencies not interested in the set of all logical consequences of a discourse
to use one of these knowledge representation formalisms especially the tractable descendants of kl one for lexical disambiguation leads to problems which disqualify language restrictions as the only means to ensure tractability of the disambiguation problem
the other measures tell us more about the test itself
the measures we give can be applied to either one
we begin by mentioning two reasons why that is not possible
rest noise shows how many bracket pairs were not tested properly
the rest NUM NUM NUM NUM is spurious sp
this is a theoretical operation thus there is no need to do this in practice
by assuming the rest is similar to a binomial distribution we can calculate the significance
we do this by looking at the comparative items yy yn ny and nn
in this paragraph we will show some examt les of measures that can be used
let cb be a set of constraints between the labels of the variables
this new compatibility measures are not limited to NUM NUM as probabilities
tlm ugh the rest of this NUM aper we often extract a quasi sig n n
the man bought the dog that chased the cat that died
the seed constitutes a representation of the initial bindings provided by the query that is used by the magic predicates to derive guards
as is illustrated in section NUM NUM this allows the avoidance of spurious ambiguities in the absence of subsumption check in case of the example grammar
this can lead to nontermination as the tree fragments enumerated in bottom up evaluation of magic compiled grammars are connected johnson forthcoming
NUM otherwise the weight of the feature constraint is NUM
we describe constraints on the morphological parses of tokens using rules with two components
we then present our approach and results
4we have left this for future work
NUM NUM determining the vote of a rule
we have statistics compiled from previously disambiguated text on root frequencies
there are a number of ways votes can be assigned to rules
our results are summarized in table NUM
table NUM results with voting constraints and root statistics context statistics
the key point here is that due to the nature of the composition operator the constraint transducers can be composed off line first giving a single constraint transducer and then this one is composed with every sentence transducer once see figure NUM
higher number of constraints higher number of features in the constraints constraints that make reference to nested stems from which the current form is derived constraints that make reference to very specific features or values
however this approach can not account for the semi productive nature of such rules illustrated with respect to the dative alternation in NUM NUM john faxed xeroxed emailed his colleagues a copy of the report and for practical lkb building there is a problem acquiring the information about which lexical entries a rule applies to
the concept of transitivity has been defined in terms of the following parameters overall effectiveness or intensity with which an action is transferred from one participant to another a there must be at least two participants for an action to be transferred
the topology of the fsm associated with a given word may be shared with other words but the specific probabilities associated with the states representing lexical entries will be idiosyncratic so that the each lexeme representation must minimally encode the unique name of the relevant fsm and a probability for each attested state lexical entry as shown in figure NUM
in this representation the states of the fsm which have been given mnemonic names corresponding to their types are each associated with a probability representing the relative likelihood that fax will be associated with the derived entry which results from applying the rule to the source entry the probabilities shown here are purely for illustrative purposes
we would expect that in general the more specialized rule will be more productive as a natural consequence of applying to a smaller class but the earlier proposal would have had the undesirable consequence that this was a fixed consequence which could not be adjusted for cases where the generalization did not hold
automatic acquisition of information from corpora is a partial answer to this problem and one which is in many respects complementary to the approach assumed here but successful acquisition of a broad coverage lexicon from a really large corpus would lead to a similar problem of massive ambiguity as we see in the case of productive lexical rules
such as the name of the hotel in the above example
and the approach is that of late stage di sarnbzguatlon
and in NUM it is an acknowledgment of the information provided
NUM s1 so we ll switch you to a double room
the model adopted here is that of a two layered finite state machine henceforth fsm
the discourse module processes the global and local structure of the dialogue in two different layers
NUM the macro structure of the dialogue up to that point
this project addresses the problem of choosing the most appropriate semantic parse for any given input
precisely speaking a fragment is a set of homonyms occupying one or more successive positions in the sentence one homonym in each position together with a directed tree defined on these homonyms as nodes the arcs of the tree being labeled with names of syntactic relations such arcs are called syntactic links
verbs nouns adjectives and adverbs which are present in the morphological dictionary but absent from the syntactic one are assigned one of the following standard entries transitive verb intransitive verb inanimate masculine noun animate masculine noun inanimate feminine noun animate feminine nolnl neuter noun adj ectiw adverv
it should be noted that grammar rules by means of special operations can change priorities of links and fragments in order to widen the search if there is a danger to lose correct fragments
for each word there must be specified a set of relevant syntactic features from the full list of NUM features a set of semantic categories from the list of NUM categories and a government pattern which expresses the requirements that must be fulfilled by the elements representing in the synts the semantic actants of the word mei uk
at first the corrector tries to improve the sentence by changing a single word in case of failure it tries to change a pair of words then a triple of words and so on
it can be said that in these experiments the difference in performance between the system described and the ideal corrector was NUM for correct sentences and NUM NUM for sentences with single distortions
function c r does not increase for r NUM behaviour of the corrector may in different ways depend on the values of c r which would result in different modes of operation
the words absent from the morphological dictionary were added to it before the experiments such words covered about NUM of all word occurences in those sentences the chosen NUM sentences were processed by the corrector
at the second and third stage attempts are also made t establish links of previous stages as they could be not established at their own stage because of missing intermediate links of tile later stages
taking this fact into account we adopted the following strategy the least r1 is found for which c r c r NUM for all r ri r rmax
well defined tree like strtlcl tlre the system ca31 rise polllls aj o sbrollg cl les lot lel er llllllll llle still al le positions in the thesa rus for unknown words the thesaurus a a bl ck box via certain functions
NUM rcb ili lcb ol ller ineth ds tip w ir lcb lseiisc lcb lisa litl rcb igtla l ioli lisiiigj w lcb irdn ira re t lcb il pro l rcb lcb rcb scd NUM NUM rcb NUM
the second argument is based on examples ill which an anaphoric reference see ms to be made to the denotation of negative sentences therefore suggesting that they have one
the problem then reduces to sequence correction problem akin to standard spelling correction problem
figure NUM structural and lea label lifl erences he tween trees
the trees that we consider have labeled terminal and non terminal nodes
for instance the first tree in fignre NUM would be represented by the vertex list sequence
our concern in this work is not the exact match of trees but rather approximate match
following some experimental results from a number of synthetic tree databases the paper ends with conclusions
the trie is then used with an approximate finite state recognition algorithm close to a query tree
this is significant considering the tiny corpus from which it was derived NUM million vs NUM million in the case of wsj
several research groups have reported the successful improvement of lms using techniques that iteratively tune the lm parameters using new samples of training data e.g.
in this approach a similarity metric is used to fmd and extract related material from the background corpus regardless of the top down classification
the second investigation concerns the empirical development and evaluation of a set of language models for the task of email speech u text dictation
the models can be interchanged between trials allowing comparative evaluation by measuring the word error rate wer produced by each model
for a given lig l and an input string x we exhibit a non ambiguous cfg whose sentences are all possible valid derivation sequences in l which lead to x
if the number of ambiguities in the initial lig is bounded the size of dl for a given input string x of length n is linear in n
therefore the number of productions is dominated by the first form and the size and in fact the construction time of this grammar is NUM n6
its language is lcb r4 lcb r kr o lcb r o rcb k NUM k which shows that the only valid linear derivations w r t
the size and the time needed to compute dl are closely related to the actual sizes of the and relations
if we assume that an a production is generated iff it is an a production or a occurs in an already generated production its production set is
the operator makes the replacement obligatory makes it optional
replacement expression NUM upper lower i i left rivet NUM
itere either u1 or ua could be replaced but not both at the same time
specifically tire price fell link will now be strongly disfavored in figure 3b since verbs rarely lalw two n n del endents to the left
we believe it is ffttil u to de sign prol al ility models indel en letrtly of tit pa rser
we il ustrate how each hypothesis is xl ressed in a depemteney framework and how each can be used to guide our parser toward its favored solution
it can not be emphasized too strongly that a grammarital rcprcsentalion de4 endency parses tag sequen es phrase structure trees does not entail any particular probability model
a likely parse is therefore one that allows a likely and consistent aln our implementation the distribution over possible disjuncts is given by a pair of markov processes as in model c
figure NUM tligh level views of model a formuhrs i NUM model l l forinul t NUM and model c lbrmula NUM
a good deal of the l arsi0 g sll ess of inoclel NUM seems to h ve arisen from its k iowle lgc of individ tiff
aecordittg to a markov l rocess with t h random choice of e ch tag conditioned ou the previous two tags
the siinplcst and astest uiodel the l cur siw generation uiodel NUM did easily i he bcsl
s structures represent more superficial properties such as case binding etc s structures derive fi om d structures via move a they are the result of movements that take place in tile latter
this suggests that even if the most strongly correlated domains are chosen it is difficult to justify augmenting the email corpus with texts selected from the bnc using this method
the prefix probability p s g l x of x given g is the sum of the probabilities of all sentence strings having x as a prefix
a rule x y1 yi lyiyi l yj can effectively act as a unit production that links x and yi if all other nonterminals on the rhs can expand to e
for the unit production matrix pu a similar argument applies since the length of a derivation is at least as long as it takes to terminate any initial unit production chain
an alternative method for making earley parsing more robust is to modify the parser itself so as to accept arbitrary input and find all or a chosen subset of possible substring parses
thus during the forward pass each state must keep track of the maximal path probability leading to it as well as the predecessor states associated with that maximum probability path
from a theoretical point of view the earley approach has the inherent appeal of being the more general and exact solution to the computation of the various scfg probabilities
an lr parser on the other hand has access to a complete list of sets of possible items computed beforehand and at runtime simply follows transitions between these sets
these processes are started whenever ac reads a leftmost node n with the same label as a leftmost leaf node in some tree in lhs r items i and ii in definition NUM
each node n of c such that n NUM state n is then inserted in chain i if state n satisfies case NUM above and is inserted in rule i otherwise
however at this point the root of lhs r2 i.e. node n NUM does no longer belong to state m27 indicating that r is no longer applicable to that node
let also i be an integer valued variable state be an associative array rule i be an initially empty set for NUM i and let h be a heap data structure
crucial to the efficiency of our algorithm each time a rule is applied only a small portion of the current tree needs to be reread by ag in order to update our data structures as specified by lemma NUM above
phon wrote syn orv subcat ni NUM np NUM rein wrote sere content agent object indices NUM figure NUM a lexical entry for wrote
2though through the rest of the paper we treat the definition as if it were used in an actual implementation the actual implementation uses a more efficient method whose output is equivalent with the result obtained by the defiifition
NUM good paper the sub structure for s1 sere object NUM good paper indices NUM good paper the goals head dtr non head dtr vmues are omitted
we present an approach to a modular use of codescriptions on the syntactic and semantic level
this would not be possible when the sem parser has to wait till the syn parser is finished
a corpus of NUM sentences turns from NUM dialogsdn the verbmobil corpus
in contrast to the previous method partial evaluation is corpus independent
top down hypotheses result from activ null ities of the sem parser trying to verify bottom up hypotheses
the colmnn semqa shows the results for the m mq arser in quasi autonomous mode
er input consists of word lattices of hypotheses from speech recognition
the pars null german verb komrne to come
this might sound strange but if one processor lm sieally
parsing these lattices is much more complex than parsing written text
no ordering of goals or variables is assumed because all choices axe weighted and a global quality function allow the system to maximize the overall efficiency of each graph
a functive has the following form
center shiflt involving p2 help you and p5 kiss you that inakes thedesired reading possible
the research on extraction of semantic relations from dictionary definitions e.g. NUM NUM has resulted in new methods for disambiguation e.g. NUM NUM
discourse processing is structured in two different levels the context module keeps a global history of the conversation from which it will be able to estimate for instance the likelihood of a greeting once the opening phase of the conversation is over
a speaker s utterance of an elliptical expression like the figure twelve fifteen might have a different meaning depending on the context of situation the way the conversation has evolved until that point and the previous speaker s utterance
we also take into account the situation where there is no possible choice either because the fsm does not restrict the choice i.e. the fsm allows all the parses returned by the parser or because the model does not allow any of them
requesc mfo rcb tell me price into the prices establishment at the estabhshmenc name holiday inn
thils they can not also explain a certain kind of ironi utterances in which hearers are not aware of any pragmatic violation
i ooking at the broken cup his wife said NUM a thank you fur washing my cup carefully
once the textual units are determined the rhetorical parser uses the procedures derived from the corpus analysis to hypothesize rhetorical relations between the textual units
for example the propositional content of NUM is t hwngr h
conversely while terminating gentzen proof procedures are available for extended lcg systems of the kind we presented here none of these handle the coordination schema and as far as we are aware the computational properties of systems which include this schema are largely unexplored
informally while rule p allows the features associated with an argument to be weakened together with the introduction and elimination rules it permits the argument specifications of predicates to be strengthened e f the subproof showing that remained belongs to category vp ap in figure NUM
the distinction between the categories npaacc dat and np accvdat and hence the existence of the apparently inconsistent categories seems to be crucial to the ability to distinguish between the grammatical 5c and the ungrammatical NUM
in order for an utterance to be ironic a speaker nmst utter in a situation sm rounded by ironic environmeut
in the sample rule an argument is required that fills the patient role
this is justified because of the limited usefulness of large reversible grammars for generation
however incremental application enforces decisions to be taken locally for each conflict set
the basic unit of the lexicon is a superentry one for each citation form holds irrespective of its lexical class
the analysis is based on a set of lrs implemented and tested on the basis of spanish and english business and finance related corpora
ss also of us department of defense attn r525 fort meade md NUM usa and carnegie mellon university pittsburgh pa
the lrs have been tested in a real world application involving the semi automatic acquisition of a spanish computational lexicon of about NUM NUM word senses
counting words within a limited window would be smoother than using strict bigrarns and eousequently less affected by the problems caused by sparse data which are inevitable when small individual text files are compared
it would be highly desirable therefore if a method could be devised whereby information from a large corpus could be combined with a smaller sample of the domain specific training data to create an optimal language model
although the rank correlation may be applied to the wfls regardless of their content it was found empirically that performance improved if function words were excluded from the contingency table
so if a lm is trained on text that is very similar to the test text then it should predict the test data well and the perplexity should be low
the next question concerns the consonantal place of articulation of the segment to the right of an if it is alveolar go left otherwise if it is of some other quality or if the segment to the right of aa is not a consonant then go right
we can represent this by setting p at this node to be e where e conventionally represents the entire alphabet note that the alphabet is defined to be an alphabet of all c c correspondence pairs that were determined empirically to be possible
lexicons phonological rules hidden markov models and regular grammars are all representable as finite state machines and finite state operations such as union intersection and composition mean that information from these various sources can be combined in useful and computationally attractive ways
thus bestpath g o d o c o a NUM the transducer c fo e rulet can be constructed out of the r of NUM trees one for each phoneme trained on the timit database
the output c is defined as the union of all of the possible expressions at the leaf node in question that aa could become with their associated weights negative log probabilities which we represent here as subscripted floating point numbers
table NUM gives sizes for the entire set of phone trees tree sizes are listed in terms of number of rules terminal nodes and raw size in bytes transducer sizes are listed in terms of number of states and arcs
variolls NUM rol onents of this view propose difl ere nt
we associate thus a predicative drs with the node advl adjoined to tp
the pair 5a 5b exemplify the contrast between simple past and progressive past in narrative discourse
motivations discussed in the accommodation literature including speakers concern for social standing and communicational efficiency are examined in the light of the results obtained
since users in the machine interpreted setting should not be concerned with social standing we might predict a lower rate of accommodation than in the human interpreted setting
however in order for naive users to accept and use computers effectively in an interactive format the restrictions placed upon them need to be as minimal and as natural as possible
to eliminate it direct relations between descriptions of fnlly formed objects are often defined
the proposal is extremely modest making use of only basic inference power and expressivity
horizontal relations have a number of undesirable features as well as requiring an external meclmnism
there are proposals in the literature which build on the idea of using underspecified entries
with to load NUM NUM and NUM the pp complement is optional
for instauce both load and stuff show locative alternation but only the former admits optional pp complements
analysis is incremental and deterministic and the procedure relies mainly on what we will call trivial type inference
on the other hand any object can be related to any other object by stipulation in an external mechanism
ps rules are annotated with procedures which pick up the correct verb type resolving clause when the appropriate complement is encountered
have potential vi tims when their iroific environments fall in one of types l NUM NUM in the ease of type NUM or type NUM an agent of NUM i ecomes a victim and i l the case of type NUM an agent of a becomes a victiin
given p expressing the p oposil ional content of u and q expressing the speaker s expected event state of affairs an utterance u alludes to the exl ectation l if it satisfies lie of the conditions shown ill table NUM
the essential idea underlying our theory is that an ironic utterance implicitly displays ironic environment a special situation which has three properties for being ironic but the hearer does not have to see all the three properties implicitly communicated in order to recognize the utterance to be ironic
for such inputs tdmt can quickly produce the same translation results with either method
figure NUM shows how an input string is parsed using our bottom up chart method
we have proposed an incremental translation method in transfer driven machine translation tdmt
presently the prototype system can translate bilingually between japanese and english and between japanese and korean
in such ases we emt loy part of speech bigrams as boundary markers
this sequence will now match tile general transfer knowledge pattern x noun verb y
in chart parsing an input string is parsed by combining active and passive arcs
ail action of eating the pizza performed by candy is expressed by tile predicate eat x a and its negation i.e. an action of not perfbrming eat x a by at x a
NUM x at y x chinatown NUM x at y x goes
a variable corresponds to some linguistic constituent and is expressed as a capital letter e.g.
the theory claims that an ironic utterance implicitly communicates the fact that its utterance situation is surrounded by ironic environment which has three properties but hearers can assume an utterance to be ironic even when they recognize that it implicitly communicates only two of the three properties
figure NUM sample translation well advp advp bien j well advp advp beaucoup k
relative clauses every relative clause is considered an argtunent of the lexeme the relative pronoun refers to
here agrs and agrv are a pair of aggregate unification specifiers that succeeds only when one of the above combinations of the feature values is unifiable
this involves associatively searching through the database tbr trees that are close to the query tree
the results are presented in able NUM it can be tree matching algorithm
this search has to be very fast if apl roximate matching is to be of any practical use
recent approaches in machine translation known as example based translation rely on searching a database of previous translations of sentences or fragments of sentences and composing a translation from the translations of any matching examples sato and nagao NUM NUM nirenburg beale and l omasnhev NUM
intervals at varying levels of recall
the errors are classified in table NUM
figure NUM uk and vk often co occur as do uk and
the one to one assumption means that linked words can not be linked again
the gdm ensures that every disambiguation module intervenes only if previous ambiguities have already been resolved
b when performing the collective evaluation of all si anaphors every inconsistent rij is suppressed
the basic focusing cycle is applied on each ee in turn and not sentence by sentence
the expected focusing algorithm is applied only on the initial ee which must not contain anaphors
in d they refers to ice cream cones in afl
the main problem is to determine the constraints that intrasentential phrases must respect in anaphoric relations
for the implementation the success rate of resolution was NUM
the focusing approach always prefers the previous sentences entities as antecedents to the current sentences
step NUM apply the expected focus algorithm to the first ee
it has been tested on a set of NUM news reports
the reason is that one does not design an slds in the domain o1 air ticket reservation which provides l alse or un founded information to cuslomers
the parser uses around NUM megabytes of memory and training on NUM NUM sentences essentially extracting the co occurrence counts from the corpus takes under NUM minutes
therefore good turing is applied to each subtree class separately that is to the s subtrees np subtrees vp subtrees n subtrees v subtrees etc
secondly the sentence is parsed by dop3 provided that subtree terminalsareallowed to mismatch only with the words that were not found in the dictionary
this lack of interest in using good turing may be due to the fact that many stochastic grammars are still being constructed within the grammar building community
or il sl illco will o the wisp is lal clle i is a NUM word si lgtllar coililtioil lic illd
tagging error rates percentage of all tags that were incorrect similarly were NUM NUM NUM NUM NUM NUM NUM NUM and NUM NUM
a searchable version of files annotated to date and a list of past tagging decisions ordered by word and by tag are at the treebankers disposal
table NUM also shows the results of the homogeneity tests
bnc i o h h4 h4l bnc i NUM g g5 g54
of course practical generations of ldgs must improve over a blind application of definition NUM
we also evaluated our method by using a constructed thesaurus in a pp attachment disan bigua tion
this is because in our disambiguation test we only need a thesaurus consisting of these NUM words
recently various methods for automatically constructing a thesaurus hierarchically clustering words based on corpus data
we then view the problem of clustering words as that of estimating a probabilistic model representing a
in this paper we propose a new method for automatic construction of thesauri
we show the result of this experiment as wordnet in table NUM
since there are 2pv i subsets of the set of llottns
NUM NUM resolving the focus of focusing subjuncts
the same algorithm identified correctly NUM NUM of the clause boundaries with a precision of NUM NUM see table NUM
the occurrences of parenthetical information are marked in the text by a p and a unique subordinate satellite that contains the parenthetical information
if the existing vp advp pattern does not give a correct translation
we introduced the notion of rhetorical parsing i.e. the process through which natural language texts are automatically mapped into discourse trees
therefore we can use them as rhetorical indicators at any of the following levels clause sentence paragraph and text
all the text fragments associated with a potential cue phrase were paired with a set of slots in which an analyst described the following
traditionally the problem of sparse data is approached by estimating the probability of unobserved cooccurrences using the actual cooccurrences in the training set
for example trajficking was found to be similar to crime because in drug contexts the expressions drug trajficking and crime are highly related
finally the predicate reevaluate forces the rule to be re evaluated with the same substitution until the destination is disambiguated completely or an incompatible answer is given
features that are common to only a subset of all represented feature structures are in the scope of the most specific type that is in common to that subset
the representations that are stored on the semantic level after the input has been parsed and processed are shown in figure NUM
to provide output functionality that can easily be adapted to new domains the predicates also offer the possibility to call tcl scripts
finally the shortest path is calculated and the result is stored in the path object as a list of line segments
the evaluation of the program consists of the subsequent evaluation of the rules in the order in which they are specified
the jomi s value of ksnnc n
this fact makes the architecture well suited for repair and rescore mechanisms that integrate scores from the speech recognizer and semantic domain knowledge
the lines in a line graph the rectangles and their orientation in bar and column graphs all play an important role in the perception of the data
figure NUM complement extraction lexical rule
in this seelion we will outline the three lexicalist linguistically perspicuous qualitatiw ly different models that we have leveloped a nd tested
calne the rchi i ail e of tile areiit to a el t uiore children
must i e minimal not itself expressihle s a concalenal ion of narrower spaus
for the analysis of sen8the reader may wonder whether one could n t get around the requirement that the lr for passive apply under subsmnption by restricting the rule to apply only to main verbs
b constituent structure and sub ategorization may be highlighted by displaying the same dependencies as a lexical tree
file result of such an application is the same as shown in fig NUM except that the direct object is placed on the slash set instead of the sub3 list
estimates based on relaxing the distance measure could also be used for smoothing at present we only back off on words
first the current estimation methods treat occurrences of the same word with different pos tags as effectively distinct types
with a beam search strategy parsing speed can be improved to over NUM sentences a minute with negligible loss in accuracy
the head to the left and right end of the constituent then the lower probability constituent can be safely discarded
a basenp or minimal np is a non recursive np i.e. none of its child constituents are nps
the overall model would be simpler if we could do without the basenp model and frame everything in terms of dependencies
most of the specifications of a lexical entry are assumed to be passed unchanged via the automatically generated frame specification
this is advantageous because postponing the execution of the interaction predicates allows more constraints on the word to be collected
for each interaction definition we can therefore check which of the flame clauses are applicable and discard the non applicable ones
in case the automata corresponding to two lexical entries are identical the entries belong to the same natural class
more specifically the linguist specifies exceptions as a special property of either a lexical rule or a lexical entry
in the paradigm of hpsg lexical rules have become one of the key mechanisms used in current linguistic analysis
finally we discussed implementation results and illustrated the improvement in time and space efficiency resulting from the covariation encoding
useful setnant ic rela tions is from this t crsl ective quil e a cidcn lcb a l
many or the annotated strings exhibit violations such as crossing links and multiple parents which iftheywcreallowed wouhl let all the words express their lexical prefe rences
consider the constraint hierarchy in example NUM example NUM a constraint hierarchy lcb m v p c parse rcb lcb fill p rcb lcb fill m rcb this ranking ensures that in optimal descriptions a v will only be parsed as a peak while a c will only be parsed as a margin
thus a cell covering a given substring of length greater than one may be filled in two mirrorimage ways via underparsing by taking a partial description which covers all but the leftmost input segment and adding that segment as underparsed and by taking a partial description which covers all but the rightmost input segment and adding that segment as underparsed
the second consequence is that overparsing operations may need to be considered more than once because the result of one overparsing operation if it fills a cell could be the source for another overparsing operation
this is true because the constraints must be local any new constraint violations are determinable on the basis of the cell category of the factor partial descriptions and not any other internal details of those partial descriptions
in the algorithm presented here the set of cell categories are the non terminals of the position structure grammar along with a category for each left aligned substring of the right hand side of each position grammar rule
the number of such blocks is the number of distinct contiguous input subsequences equivalently the number of cells in a layer which is on the order of the square of the length of the input
as an illustration consider the grammar in examples NUM and NUM this illustration is not intended to represent any plausible natural language theory but does use the peak margin terminology sometimes employed in syllable theories
for formally regular position structure grammars he defines a local constraint as one which can be evaluated strictly on the basis of two consecutive positions and any input segments filling those positions in the linear position structure
a tight interaction between these two components was created which was used to model a synchronization point at every frame in the speech input i.e.
the preferred interpretation of he and the man in u are fred and max respectively rather than the contrary
once the game is defined however both players must take into consideration the entire maximal connected subgraph containing the content she wants to convey or the message she wants to interpret
then rule NUM predicts that fred can not be realized by the man if max is realized by he the same prediction that we derived above
in fact an embedded belief turns out wrong if it implies rs mlc NUM for every c in the embedded context
that is when the players have common knowledge that message m was sent they may be able to detect errors in their embedded beliefs
fortunately this equalization is very stable as long as the success of communication is the only source of positive utility for both the players
for instance when s tells r that it is raining r will learn that s wants to make r believe that it is raining
subject direct object indirect object other complements adjuncts the highest ranked element of cf u is called the preferred center of u and written cp u
so for example adjunction of flowers in the antecedent clause of NUM to the np fragment chocolates in the ellipsis site produces the lf structure 12a which is interpreted as 12b
figure NUM ot evaluation for tagalog infixation
to be similar to the input
since no manually tagged training data is available for our corpus the tagger s default rules were used these rules were produced by brill by training on the brown corpus
these are a factor favoring left branching which arises from the formal dependency construction and factors allowing for naive estimates of the varia null tion in the probability of categories
in this study not only has the technique proved its worth by supporting generality but through generalisation of training information it outperforms the equivalent lexical association approach given the same information
for reasons made clear below only sequences consisting entirely of words from roget s thesaurus were retained giving a total of NUM test triples
to ensure that the test set is disjoint from the training data all occurrences of the test noun compounds have been removed from the training corpus
while using windowed co occurrence did not help here it is possible that under more data sparse conditions better performance could be achieved by this method
in all experiments the dependency model provides a substantial advantage over the adjacency model even though the latter is more prevalent in proposals within the literature
let county n1 n2 be the number of times a sequence wlnln2w4 occurs in the training corpus with wl w4 at
the total number of states in the las compiled fl oln them was NUM
definition NUM restriction schema a restriction schema rs is a set of paths
feature structure obtain s ex d o and
null a feature structure of the above form corresponds to a clause in prolog
selected japanese sentences from a newsl at er asahi shinbun
for the first case consider a rule schema with the following feature structure
note that a node is unified if its structureshared part has a counter i art in f2
this section describes the parsing process of the sentence my colleague wrote a good paper
a dcp can be seen as a logic program language whose arguments are feature structures
basically it consists of all expected answers and some standard words that are always active
especially we have shown that we can deternfine the referents of zero pronouns to some extent with our linguistic constraints like the con straint of the japanese conditionals
at lirst note that the hearer namely the use r is the agent of cite requested action if the matrix clause is a re luest form
the former type is problematic because the rul orm which is the normal inflection form of verbs and describes an action is ambiguous in its meaning
on the other hand the following sentence which does uot have the verbal suffix of possibility are in the matrix clause has a different interpretation
on the other hand a third paj ty can NUM e the subject because a sentence whose subject is a third party does not express any volition invitations requests or injunctions
roughly speaking in tile active voice the subject is the nominative on the other hand in the passive voice the subject is the nominative of the corresponding sentence in the active voice
we shouhl consider two types of information as the parts of ontology the properties of the objects in manuals and the discourse situation that is characterized by linguistic roles like a writer and a reader
only that it is possible for the subject of the sentence to do the action but also thai the subject has their choice of whether to do the action or not to do it
shows l two individual events occur with ie l assing of tile time or NUM an event which is expected to occur on the uncertain assumption expressed in the subordinate clause
as an example the reader may want to check the translation in figure NUM and furthermore verify that the reverse translation does indeed take us back to the original modulo renaming of variables and labels udrs
to express these appropriately is crucial especially in human machine dialogue since they contribute to the NUM success of the interaction in a major way
NUM in the systemic functional model the strata assumed are context extra linguistic semantics and grammar linguistic
thus this approach allows prosodic features to be controlled by various factors other than syntax e.g. by the information structure such as focus background or topic comment structure
reject re exch resp statement a NUM quest table NUM notation and indicate choices in par
la neutral lb emphatic 2a neutral 2b negative 3a weak contrast 3b strong contrast 4a neutral 4b negative NUM assertive clarifying
it is in the area of combinations of dialogue moves that we find reflections of speaker s attitudes and intentions and hearer s expectations as determined by the context
more concretely it will be shown that the factors just pointed out are logically independent parameters that in different combinations constrain the selection of a particular tone
our goal is to determine more precisely what is comprised by them and to arrive at a refinement of the general architecture we have presented in section NUM
we base our derivation of mappings between the strata on the following sample dialogue and its cor analysis ll from the domain of giving out train information
the speech function is statement since the system tures we choose declaratwe stating neutral nonemphatic non contrastive hence tone la
and ask t area x while the object of k is not assigned a case
the parser deals only for the moment being with logical coordinations and or but
comments generally speaking comments do not share any microsemantic relation with the sentence they are inserted in o draw a line that s it on the right for instance the idiomatic phrase that s it is related to o at the pragmatic level and not at the semantic one
these experiments were achieved on the literal written transcription of three corpora of spontaneous speech table NUM which all correspond to a collaborative task of drawing between two human subjects wizard of oz experiment
structure of lhe primb g network ifi j if the case z coitcs onds to a compulsory argument of the lexeme i or if the latter shoukl fulfill c alone
then they are parsed like any coordinationdeg ellipses and interruptions the principle of relative completeness is mainly designed for the ellipses and the interruptions our parser is thus able to extract alone the incomplete structure of any interrupted utterance
the inner synaptic weights of the case based sub networks represent the relations between the priming and the primed words NUM NUM t mmax if iandj share a microscmanlic relation which corresponds io the case z
the aim has been to develop a realistic application oriented prototype whose dialogue management allows users to perform their reservation task in spontaneous and natural spoken language
in the user test the speech recogniser was simulated whereas the other system components were the implemented ones the system was tested with NUM external users
the system should behave as a perfect expert vis vis its users within its declared domain of expertise otherwise it is at fault
this refinement activated dss dlc for csa NUM in NUM q3
this refinement activated dlc for csa NUM in NUM q1
they are combined because they are the first two messages
this refinement activated dlc for csa NUM in NUM q1
fig NUM is an overview of plandoc s architecture
the first aggregation step is to identify semantically related messages
this refinement activated all dlc for csa NUM in NUM q3
the process is not as trivial as might be expected
the ontologizer adds hierarchical structure to messages to facilitate further processing
this is done by grouping messages with the same action attribute
now tile two phases of our parsing algorithm can be described in more detail
table NUM shows that NUM of dependencies do not cross a verb giving empirical evidence that question NUM is useful
question NUM allows the parser to prefer modification of the most recent verb effectively another weaker preference for right branching structures
each word in the reduced sentence with the exception of the sentential head announced modifies exactly one other word
equations NUM and NUM define p b s and af dis b
conditioning on the exact distance between two words by making aj hj hj j leads to severe sparse data problems
however other features of a sentence such as punctuation are also useful when deciding if two words are related
we believe that lexical information is crucial to attachment decisions so it is natural to condition on the words and tags
our system tries to resolve the lexical ambiguity ot nouns by finding the combination of senses from a set of contiguous nouns that maximises the conceptual density among senses
it selects the concept c with highest conceptual density step NUM and selects the senses below it as the correct senses for the respective words step NUM
NUM generalizing this view in the obvious way whole datr descriptions can be thought of as denoting functions from nodes to partial functions from paths to values
NUM opted for off line compilation of their NUM NUM word datr lexicons into pairs of on line lexicons one of which was encoded with bit vectors for speed and compactness
tile nodes eliminated by res must appear in sub fs r
the default mechanism of datr provides for generalization across sets of atoms by means of path extension and is the preferred mechanism to use in the majority of cases
a ruh s henl consists of the following two items
NUM thus for example the path mor plur acc is a gratuitous extension of the path mor plur for english common nouns since the latter are not differentiated for case
in this case processing continues without results from that slot
some p p th n t f is prefixed by m ls
depending oil the article these values range from NUM NUM NUM NUM to NUM NUM NUM NUM the mean being NUM NUM NUM NUM
the system s rllll til te it a sun st arc workstal ion unix sun os NUM NUM NUM
tile major part of this work has been drawn froln the author s dissertation at the centre for cognitive science university of edinburgh uk
while a simple lead method is more likely to produce higher readability judgments the advantage of the tf idf method for abstracting is its superiority in terms of capturing content relevance
then a sign can be obtained by unifying a sub structure and the corresponding core structure
the second element of the args list in figure NUM
although they exhibit no parses with respect to their grammars it can be assumed that they feature only rudimentary tag and non terminal vocabularies
finally for nonterm nal nodes general information about the number of children span constituent boundaries etc is available
among the features considered were clause dependency and conjunction type
we can therefore distinguish two different types of evaluation as to how well expert humans do at parsing using the grammar consistency and accuracy
a detailed examination of those models shows that the syntactic models are better than the parser s while the semi tic models are worse
since the atr english grammar was created specifically for use in machine parsing some of its features are designed expressly to facilitate parse prediction
he third annual long branch new jersey rod and gun club picnic and turkey shoot or high fidelity equipment
we have found that a greedy search which chooses the most likely outcome for each parsing step usually finds a good candidate parse
but simple extensions of well known parsing algorithms presuppose a random search through the space of open lexical classes to get the candidate category
but for a production with inverted orientation the 2readers of the papers cited above should note that we have switched the roles of english and chinese here which helps simplify the presentation of the new translation algorithm
but it may sometimes be useful to place probabilities on n ary productions that vary with n in a way that can not be expressed by composing binary productions for example one might wish to encourage longer straight productions
as a consequence it would be necessary to enumerate a specific set of linear precedence lp relations for the language and moreover the immediate dominance id productions would typically be more complex than binary branching
but unlike the word alignment model to accommodate the bigram model we introduce indexes in the recurrence not only on subtrees over the source chinese string but also on the delimiting words of the target english substrings
a large part of the implicit function of such parameters to prevent alignments where too many frame arguments become separated is rendered unnecessary by the btg s structural constraints which prohibit many such configurations altogether
this analysis indicated that the language employed in these different sections of the text varies greatly
but perhaps most importantly our goal is to constrain as tightly as possible the space of possible transduction relationships between two languages with fixed wordorder making no other language specific assumptions we are thus driven to seek a kind of languageuniversal property
we have judged only whether the correct meaning as determined by the corresponding english sentence in the parallel corpus is conveyed by the translation paying particular attention to word order but otherwise ignoring morphological and function word choices
in the introductory section we argued that this approach can not be correct in principle because of the problem of nonce senses
this might be necessary for example to model the relative frequencies of er vs ee suffixation since although the latter
from a computational perspective an equally acute problem is the proliferation of senses that results when lexical rules are encoded as fully productive
we can model this aspect of language use as a conditional probability that a word form will be associated to a specific lexical entry
the majority of implemented nlp systems have either simply listed derived forms and extended senses or treated them using lexical rules as redundancy statements
however further work remains to be done on acquiring sense frequencies and productivity measurements before evaluation in a full system is feasible
in the absence of other factors it seems very likely that language users utilize frequency information to resolve indeterminacies in both generation and interpretation
resnik NUM could lead to an estimate of frequencies for word senses in general with rule derived senses simply being a special case
the productivity figure could be adjusted to allow for item frequency within classes but we will not discuss this further here
thus neither the interpretation of lexical rules as fully generative or as purely abbreviatory is adequate linguistically or as the basis for lkbs
these rules may contain typed variables
NUM NUM generating noun phrases and clarification questions
our first example is taken from the map application
if for example the answer to a clarification
for example the rule displaying every object is given
also by dealing with best only substructures the explosion of structural ambiguity is constrained and an efficient translation of a lengthy input can be achieved
by comparing the preliminary experimental results from the former top down method and those from our new method we will demonstrate the usefulness of our new method
then three recognition processes are provided paragraph recognition sentence recognition and word recognition
for some pattern types a generalized representation of the matched sequence is computed and stored in an attribute cony
the tag provided for this string will be the same as for any other cardinals
NUM the remaining substring of the match is tagged as a proper noun and stored
we maintain that user interaction combined with some table lookup is the only viable approach to the robust tagging of free texts
within our project reimplementation using a more powerful tool per1 is taking place allowing filrther extensions to the flmctionality
but the definition of measure disallows the tagging of zweiundzwanzig as a measure expression
it has been integrated within the text handling procedures of the alep system after sentence recognition and before word recognition
awk is a programming language specifically designed for in this type of string manipulation matching replacement splitting
we used the two follow null ing heuristics NUM has it already been tagged as a proper noun
lfrom this result of the investigation we determine to apply to the zero pronoun resolution the following heuristics that are concerned with conjunctive postpositions
null class c ga but tile agreement disagreement depends on tile context and does not have any tendency
it also provides a reference position the cu ent node i.e. the node about which a prediction is being made
it is easy to navigate from any node to previous nodes parent child nodes and word tag nodes relative to the node s constituent boundaries
we also considered filtering the parses considered by the atr parser to ensure they satisfied certain constraints implied by the source treeb n parse
there are usually rnany values for m1 and m2 that satisfy this condition
therefore their grammar keeps the punctuation and part of speech rules separate but still allows them to be applied in an interleaved manner in effect finding the happy mediuin between the two extreme approaches
here are some examples of the inappropriate rule patterns
there are three existing hypotheses to choose from
its use is very infl equent though
there are several problems for deciding significance for bracket accuracy and bracket recall
the aphb corpus contains NUM sentences in which both young and old modify man e.g. in bihzad s paintings we see people and animals as individuals rich men and poor men old and young the elders in the mosque and the herdsmen camping among their horses in the fields
fo de null termine the fsv of the vp the ground equation 4b must be solved for which de is a solution
since simple variable sharing does not capture the unification of these objects we instead employ unify NUM
it can be employed as a standard vehicle for the interchange of content information about narrative documents
computation of the linking relation from a set of cf syntax rules is straightforward
what is the advantage of predictive linking as discussed above in NUM
it translates a small fragment of cobalt news milan october NUM NUM
firstly since all the phrasal rules are excluded from the specimization process the coverage loss associated with missing combinations of phrasal rules is eliminated
one problem with the acquisition of reliable estimates of such probabilities is that many possibilities will remain unseen and will therefore be unattested
NUM aujourd hui pierre ne possbae pas de voiture
other cases of errors will be likewise evaluated
in s structure ne must adjoin to the verb as a clitic
it is the weight and not the color of a load that is functionally relevant the thickness and thus weight of a harness that bears on the speed and load bearing potential of the draught animals fitted with it and the heaviness of a vessel that is relevant to the speed ease of handling load bearing potential and imperviousness to damage that is pertinent to military cruisers
the second topic is a more radical departure and can be viewed as an attempt to make interleaving of parsing and pruning the basic principle underlying the cle s linguistic analysis process
even more interestingly table NUM shows that real system performance in terms of producing a good translation is significantly improved by pruning and is not degraded by grammar specialization
the system tg NUM described in this paper can be smoothly integrated with deep generation processes it integrates canned text templates and context free rules into a single formalism it allows for both textual and tabular output and it can be parameterized according to linguistic preferences
it corresponds to an infinitival vp covering a direct object an optional temporal adjunct an optional expression for a duration such as for an hour an optional local adjunct such as at the dfki building and the infinite verb form
let us call the sequence of strings generated by previous actions its pre context the set of string sequences generated from the elements of the conflict set its ego and the sequence of strings generated from subsequent actions its post context
in the near future the system will be used within an nl based information kiosk where information about environmental data must be provided in both german and french language including tabular presentations if measurements of several substances are involved
template actions under template NUM include the selection of other rules rule 0ptrule executing a function fun or returning an ascii string as a partial result
however different gil structures may for a tgl rule lead to different sets of follow up c rules
each time a backtrack point is encountered during processing an entry into a global table is made by specifying its pre context which is already known due to the left to right processing a variable for the ego which will collect the sequences of strings generated by the elements of the conflict set and a variable for the post context which is unknown so far
lengthrelated not long senses of short are indicated by nouns that are time period term period day duration minute month night time weekend in the co occurrence sentences but this attribute is subsumable under concrete
the remainder of the paper is divided into four sections one describing the overall structure of our models and one for each of the three major components of parsing semantic interpretation and discourse
NUM the constrained space of candidate parses t received from the parsing model combined with the full space of possible pre discourse meanings ms is searched for n best candidates according to the measure
this is why we intend at present to inserl directly some ordering constraints spontaneous speech never violates
thus each frame will be assigned few equations which will characterize some ordering priorities among its arguments
open questions g are on the contrary introduced explicitly by an interrogative pronoun which stands for the missing argument
back priming generally speaking the priming process provides a set of words that should follow the already uttered lexemes
relational priming the priming activities are then dispatched mnong several sub networks which perform parallel analyses on distinct cases fig
the final result of these steps is the n best set of candidate pre discourse meanings scored according to the measure p m s t p wit
searching the discourse model begins by selecting a meaning frame me from the history stack h and combining it with each pre discourse meaning ms received from the semantic interpretation model
corresponding pairs of input and output x i vectors are computed from these annotations which are then used to train the NUM statistical decision trees
the dialogues were totally unconstrained so that the corpora are corresponding to natural NUM spontaneous speech
its robustness is noticeably higher on the third corpus which presents a moderate ratio of ungrammatical utterances
given a string of input words w and a discourse history h the task of a statistical language understanding system is to search among the many possible discourse dependent meanings mo for the most likely meaning m0
NUM the constrained space of candidate pre discourse meanings ms received from the semantic interpretation model combined with the full space of possible post discourse meanings mo is searched for the single candidate that maximizes
that is actually all that it allows the lexicon writer to do
NUM this approach is due to recent unpublished work by jim kilbury
for example the definition of the passive form of sew is
here mor past participle is given as the sequence mor root en
likewise the morphology of be can be specified as follows n
this ensures that passive morphology is undefined for verbs not syntactically passive
we will comment further on implementation matters in section NUM below
there remains one last problem in the definitions of wordl and word2
on the basis of the user defined head relations the reflexive transitive closure over head relations is calculated
it is not yet ready for immediate commercial applications but it is neither very far away
optimal choice of head relations pays off in a gain in efficiency by several factors of magnitude
this value is coshared with the value of the string feature of the lexicon entry
null head information is propagated from head dtr to mother so is semantic information
2it should be mentioned that we are referring to the german grammar built in the ls gram project
long distant discontinuities popular in theoretical linguistics did not play a role
in generm most of the known linguistic phenomena occur in all known variations
it is shown that for a large class of grammars the subsumption check which often influences processing efficiency rather dramatically can be eliminated through fine tuning of the magic predicates derived for a particular grammar after applying an abstraction function in an off line fashion
this results in the following new rule which uses the seed for filtering directly without the need for an intermediate filtering step note that the unfolding of the magic s literal leads to the instantiation of the argument vform to finite
of filtering many approaches focus on exploiting specific knowledge about grammars and or the computational task s that one is using them for by making filtering explicit and extending the processing strategy such that this information can be made effective
the head corner approach jumps top down from pivot to pivot in order to satisfy its assumptions concerning the flow of semantic information i.e. semantic chaining and subsequently generates starting from the semantic head in a bottom up fashion
as becomes apparent upon closer investigation of the abstract unfolding tree in figure NUM the magic predicates magic sentence magic s and magic vp provide virtually identical variable bindings to guard bottom up application of the modified versions of the original grammar rules
this is illustrated on the basis of the adapted version of rule NUM np p0 p npsem index magic rip npsem index pn p0 p npsem
though this rule is not cyclic it becomes cyclic upon off line abstraction null magic vp vform csem i NUM ssem magic vp vform csem2l ssem
by bringing filtering into the logic underlying the grammar it is possible to show in a perspicuous and logically clean way how and why filtering can be optimized in a particular fashion and how various approaches relate to each other
in natural language processing filtering is used to weed out those search paths that are redundant i.e. are not going to be used in the proof tree corresponding to the natural language expression to be generated or parsed
in order to illustrate how the magic predicates can be adapted such that the subsumption check can be eliminated it is necessary to take a closer look at the relation between the magic predicates and the facts they derive
for these reasons list based matching schemes do not achieve desired performance levels
NUM s nl 100k f2 s type sec
we then make the joint unions of initial and extended middle subsequences NUM
although we are still in the development phase we will briefly describe our experience of adapting smes to this new domain
using basic edges the recognition part of an fst is then defined as a regular expression using a functional notation
the shallowness of the template construction instantiation process depends on the weakness of the defined fst of an fcp
the fragment combiner is used for recognizing and extracting clause level expressions as well as for the instantiation of templates
a transducer encoding this behavior can be generated as sketched in figure NUM
however the use of such an approach is not entirely beyond question
the parser is now almost certain to make a second mistake namely the dog waited for his master on the bridge
during the last few years large treebanks have become available to many researchers which has resulted in researches applying a range of new techniques for parsing systems
whatever value we take the significance level o lcb the difference between a and b corresponds to being NUM NUM standard variations away from the expected value
ignoring certain bracket pairs corresponds with the fact that some constituents relate to little and some to much ambiguity making some suitable for comparison and others not
it is not in fact there are both very easy and very hard bracket pairs with chances w rying from very small to very high
errors that are related sneh a s one wrong attachment that causes a number of crossing errors as was shown in our test by pinheritance
the data resulting from the test may be a general data fl om all bracket pairs or b data on specific structures i.e.
this would probably produce a crossing error since the treebank would probably contain the pair the dog waited for his maste on the bridge
these compositions enable complex text analysis to be performed by a single transducer
because the sem parser passes its semantic representation to other components it makes further sense to guarantee total correctness here
an evaluation of anaphor generation in chinese
figure NUM referring expression component in
verbmobil is built up by two sorts of components
on the other hand it is much easier for humans to decide how specific sentences should be analyzed
other more complex schemes could be developed which for example took account of the average probability of the output of a lexical rule
even with a NUM million word corpus some words occurred very infrequently and others which were found in roget s and or wordnet were absent completely
however all unseen events will be assigned the same probability within each distinct distribution and this is at best a gross estimate of the actual distribution
active npslgn npsign p agt caus p pat aff obj
similarly it should not be necessary to explicitly encode the fact that the conversion rule does not apply to an already derived form such as primer
in order to implement the approach described it is necessary to acquire probabilities for attested senses and to derive appropriate estimates of lexical rule productivity
the standard formalization of lexical rules entails that derived entries will exist without exception for any basic entry which is compatible with the lexical rule input description
for instance one standard approach to smoothing involves assigning a hypothetical single observation to each unseen event in a distribution before normalizing frequencies to obtain probabilities
the fldl acquisition algorithm is as follows given a verb check i evin lass
many multilingual nlp applications need to translate words between different languages but can not afford the computational expense of modeling the full range of translation phenomena
usually this is the desired behavior but words like english auxiliary verbs are sometimes used as content words giving rise to content words in french
the ibm models are directional i.e. they posit the english words that gave rise to each french word but ignore the distribution of the english words
then two word tokens u v are said to co occur in the aligned segment pair i if u e si and v e ti
if there is another co occurring word token pair u v such that l u v exists then repeat from step NUM
the variables that we use in our estimation are summarized in figure NUM the linking algorithm produces a set of links between word tokens in the bitext
an 3note that k u v depends on the linking algorithm but n u v is a constant property of the bitext
the estimation method uses a pair of hidden parameters to measure the model s uncertainty and avoids making decisions that it s not likely to make correctly
all of this itlforination is required to generate expressions of the action but NUM resenting it would overly complicate the graph
that the main nser goal that of saving a document is given as a title to the series of steps
because the nodes are mouse sensitive it allows the author to iifitiate construction and maintenance functions by clicking on the appropriate nodes in tile graph
in line with this we noticed that certain elements of the model were also present in the specifications developed in user interface design environments
we are also looking at a tighter integration of the design and documentation processes one in which tile individuals involved work together during design
the draft text viewer the author may draft multilingual instructions oil any portion of tile procedural structure at any point in the specification process
once complete the text plans are passed to the tactical generator which generates t he actual text in english and french
an exception is the wor 1net in which the distinguishing ea tures
more powerful grammar formalisms would generally require either a structural description or complex feature structures
this sense the entire translation algorithm is not guaranteed to run in polynomial time
the basic strategy for choosing a candidate derivation sequence from ambiguous parses is as follows
here m can be larger than NUM n if we generate every possible translation
this will enable us to specify such syntactic dependencies as agreement and subcategorization in patterns
to simplify the example let us assume that we have the following preterminal rules
since a valid derivation sequence in t is always a valid derivation sequence in g the proof is immediate
furthermore we will show that our framework can be extended to incorporate example based mt and a powerful learning mechanism
our experience is that such preference rules depend on the kind of the text one is disambiguating
this kind of revision of reading cmmot be rcl res nl ed
f rom the preference NUM lie preferably refers t lohn
coilsiders msignmenl s of truth value for formulas in NUM rioritized circumscril tion
shouhl be satisfied as much as possible for every a and i
imtting stronger preferences into a stronger hierarchy of l references
deli ldl itm mm scnl mlic dctinil ion
but he gave the telescope to the man this morning
all the words that have wrong lacking doubtful or more than NUM competing analyses are considered as bad
therefore we consider it fair to compare only our results on tagset2 with the scores of the mentioned t ls
e.g. determiners can be easily skipped which enhances the ditiiculty to distinguish a noun from certain conjugated verbal forms
attention is also paid to the main datastructures a lexical database and feature bundles implemented as directed acyclic graphs
communication in large distributed ai systems for natural language processing
in this respect we deviated fi om the original model
a special component takes over name service functiorrs
in this case the notification is simply delayed
the system interprets the respective utterances into english
NUM l he experimental system architectm e
thermore concurring channel requests do not inteffer
NUM the final stage is to use the simulated annealing algorithm to optimise the dictionary deftnition overlap for the remaining senses
NUM this research we assume that as t rcb ame instances with the same head are generated by a joint distribution of type i x x NUM where index y stands for the head and each of the randonl variables xi NUM NUM n represents a case slot
there were NUM examples in the test data verb nounl prcp no an NUM pattern in which tile two slots a rg2 and prep of verb are determined to be positively dependent and their dependencies are stronger than tile threshold of NUM NUM
we see that using tile information on dependency we can significantly improve the disambiguation accuracy on this part of the data since we can use existing methods to perform disambiguation for the rest of the data we can improve the disambiguation accuracy for the entire test data using this knowledge
thus provision of an efl ective method of learning de pendencies between as slots as well as investigation of the usefulness of the acquired dependencies in disambiguation and other natural language processing tasks would be an inll ortant contributiota to the fie ld
our experimental result verifies the validity in practice of the assumption widely made in statistical natural language processing that class based case slots and also word based case slots are mutually independent at least when the data size available is that provided by the current version of the penn tree bank
this included occurrences of periods commas colons semicolons etc NUM the type of usage sentential discourse or both
we have taken hirschberg and litman s research one step further and designed a comprehensive corpus analysis that enabled us to improve their results and coverage
the locations in texts that were labeled as clause boundaries by at least two of the three judges were considered to be valid clause boundaries
in order to deal with the ambiguity of discourse the rhetorical parser computes a weight for each valid discourse tree and retains only those that are maximal
such an assignment will only create problems at the formal level as well because then discourse structures can no longer be represented as binary trees
also the discourse trees that we build are very constrained structures see section NUM as a consequence we do not overgenerate invalid trees as sumita et al do
a summarization program that uses the rhetorical parser described here recalled NUM of the sentences considered important by NUM judges in the same five texts with a precision of NUM
typos of directed aros subj0cl object and acl ion farmer that owns a donkey beats it
controls represent a fltrther imln ovement on distributedness since they reduce the number of reqnired event nodes without mfecting richness
constitute the world it believes in and thus may be either extensional or intensional
l igure NUM figure NUM a semnet event for every
this price is justified as distinguishing intensional and extensionm concepts is important in many situations
it is possible for lolita to believe that another agent believes some relation to hohl
such misunderstandings will occur unless the hammer is correctly understood as intensional and distinguished in the representation from extensional hammers
a relation may be not only hypotheticm but also inteusionm john believes he needs a hanlmer
this discussion will e cus more specifically on l he last t wo rileria
since the grammar is assumed to be consistent and without useless nonterminals all partial derivations can be completed with probability one
such productions do however require special attention and make the algorithm and its description more complicated than otherwise necessary
the one time cost for the matrix inversions to compute the left corner and unit production relation matrices is also o r NUM
the completion step again dominates the computation which has to compute probabilities for at most o n NUM states
relative to fl fl is missing the probability of expanding y which is filled in from
precisely the same approach can be used in the earley parser using the fact that each derivation corresponds to a path
the matrix p is derived from the scfg rules and probabilities either the left corner relation or the unit production relation
tasks NUM and NUM require one more reverse pass over the chart constructed from the input
nonterminal x was expanded starting at position k in the input i.e. x generates some substring starting at position k
to potential mates r a strong deer will grow extra large antlers to demonstrate its extra survival competence with this handicap
natural language meaning games are not cheap talk games either because we must take into consideration the costs of content message pairs
to derive all of them which are right in a unified manner requires further extensive study
it seems reasonable to assume that the success of communication is the only source of positive utility for any player
ux reflects the grammar of the language which might be private to s or r to various degrees
it is not just the success of communication but also various other factors that account for the players utility
let c be the set of semantic contents and p the probability distribution over the linguistic reference to the semantic contents
if the players want something like common belief NUM however meaning games are not signaling games
that is meaning games in natural language would normally involve discrete sets of semantic contents and messages
the approach allows that the parsers never need to exchange analysis results in terms of structures as the parsers should always be able to reconstruct these if necessary
the parsing process mainly consists of constructing the tree described by tile completion history using the semantic counterparts of tile rules which led to a syntactic hypothesis
theoretically this might not pose a problenl since the intersection of two infinite sets of parse trees nfight be finite
a sign is a structure incorporating information from all levels of linguistic analysis such as phonology syntax and semantics
obviously this is only possible after some initial information by tile syn parser since the s m parser is not directly connected to the input word lattice
the most straighttbrward way to guarantee soundness is siml ly by elnploying the full size grammar ill one of the two parsers
we have used this scheme ill that the sem parser oi erates oil the full size grammar whereas the si eech parser directly conlnmnicates with tile word recognizer
the underlying observation is that constraints in such grammars can play different roles genuine constraints which relate directly to tile grammaticality wellformedness of the input
features that occur only once on top of the input feature structures do not specialize the information in the resulting structure actually the values of these features
in case that the grammarian is unaware of these constraints it is at least possible to determine them relatively to a training corpus simply by counting unifications
recall of the classifier percentage of all nouns that are classified NUM is on average among different larger corpora NUM NUM tokens about NUM to NUM
clearly this dialog is ill formed in that 6b is no appropriate correction for 6a
NUM l br clarity we have simplified the semantic tepresentation of 3b nothing hinges on this
in this paper we have argued that higher order unification provides an adequate tool for computing focus semantic values
in what follows we assume a restriction similar to the dsp s primary oeeurren e restriction l ah ymple et al NUM s the occurrence directly associated with the focus is a primary occurrence and any solution containing a primary occurrence is discarded as linguistically invalid
indeed the hou approach can be shown to provide a uniform treatment of quasi and proper soes cf
we have categorized the errors occurring in vpeal
fheu the next step towards a theory of punctuation can be carried out namely the analysis of punctuation for its semantic flmction and content
NUM specific vs general verb one of the two verbs has a more specific meaning null 9e charge the accumulator with nitrogen
we should stress that when designing the lexicalisation component of a multilingual generation system one should be careful in deciding how much importance should be given to such a contrastive analysis
this texical class encloses semantically poor items like do carry out in english and effectuer proc der in french which are combined with predicative nouns to form complex predicates
the problem is that sometimes these attributes can not take an adverbial form anti in the analyzed procedural texts it seems that this limitation is an important motivation for using operator verbs
if one speaker has uttered a request for information we expect some sort of response to that an answer a disclaimer or a clarification
there are situations where the path followed in the two layers of the structure does not match the parse possibility we are trying to accept or reject
they are task oriented dialogues in which the speakers have specific goals of carrying out a task that involves the exchange of both intbrmation and services
each one of those states contains a fsai itself which determines the allowed speech acts in a given subdialogue and their sequence
in that case the processor will try to jump to the upper layer in order to switch the subdialogue under consideration
if this is a problem for any human listener the problem grows considerably when it is a parser doing the disambiguation
the approach is to combine discourse information with the set of possible parses provided by the phoenix parser for an input string
let us look at another example where the use of information on the previous context and on tile speaker aiternance will help choose the most appropriate parse and thus achieve a better translation
in example NUM we will know that okay is a prompt because it is uttered by the speaker after he or she has made a suggestion
this statistic not only measures agreement but also factors out chance agreement and is used for nominal or categorical scales
we coded for two function features intentional ity and awareness which we will illustrate in turn using to refer to the negated action
we did this by computing x NUM statistics for the various functional features as they compared with form distinction between dont and neg tc imperatives
in this section we will start with a discussion of our corpus and then detail the function and form features that we have coded
we discuss our coding schema which takes into account both form and function features and present measures of inter coder reliability for those features
as we mentioned in section NUM however there are also many cases such as example NUM that an utterance can be ironically interpreted even though all the three components can not be recognized by the hearer because the hearer s mental situation differs from the speaker s one
furthermore previous theories assume echoic irony like 5a to allude to other person s thoughts or utterances but our theory contends that such irony alludes to a speaker s exi ectation that the speaker wants the hearer to know the hearer s utterances or thoughts are false
case of 5a after recognizing peter s utterance 5a to be iron jesse turns out to know that peter drinks jesse s t receding uttermme is absurd and tries to confirm peter s emotional attitude by interpreting 5a ironically
the phoneme inventory is of size NUM in both cases including the null phoneme but excluding stress symbols
this takes the product along each possible path from start to end of the mapping probabilities for that arc
a node of the lattice represents a matched letter li at some position i in the input as illustrated in fig NUM the node is labeled with its position index i and with the phoneme which corresponds to li in the matched suhstring pim say for the mth matched subsiring
results for the testing of lexical words are shown in table NUM again there are consistent performance differences with the standard d n model worst and the mapping probability mp mode best
pronunciation by analogy pba is an emerging technique for text phoneme conversion based on a psychological model of reading aloud
it is this lacking argument which makes up the conclusion part of the discourse relation h6 in NUM
for the semantic analysis a version of discourse representation theory is used which can express underspecification and take compositionality into account
other labels for drss should be subordinated to the discourse relation element in the way in which each of them is unambiguously subordinated to one of its two holes
since every discourse relation has two scope domains this observation leads to the following possibilities of scopal relations for fig NUM NUM
figure NUM a graphical representation of the sentence in figure NUM plausible resolution possibility for the sentence of fig NUM
the set of constraints is divided into alfa conditions and leq less or equal conditions alfa conditions define presuppositions and anaphorie relations
therefore what is subordinated to the hole introduced by the mode predicate amounts to the matrix clause of the given sentence
not only the syntactic modality auxiliary noda but also the discourse particle dakara includes a part which is bound sentence externally
discourse relation particles like dakara therefore belong to the former fig NUM subordinate explanation relations like noda belong to the latter
in NUM the scope relation is also influenced by antecedent resolution of the temporal local modification which is needed from the syntactic information
for this kind of cases having a linear or a branching obliqueness makes no difference tbr the definition of o command as such
these predictions are sketched in the following contrast schemata where si prsprio is a reflexive ruled by principle a NUM a
second i present empirical justification i r the adoption of a non linear order or the arg s wflue
and binding obliqueness rest on the analysis of two linguistic phenmnena reflexives in toba batak a western auslrenesian language and
the analysis of case marking agreement and word order phenomena in japanese causatives reveals that this construction exhibits properties of a single clause sentence
the authors elucidated its particulars namely that zui is inherently animate and ambiguous between a discourse pronoun and a syntactic z pronoun
as to the contrast in NUM principle b is satisfied in the lower arg s list where the pronoun is locally o tyee
however if they are in the same list assuming a linear or a branching obliqueness hierarchy makes a difference
deciding whether the binding theory should include principle a or a subject principle depends thus on the language which it is being applied to
selection of patterns in the derivation sequence accompanies the construction of a target chart
we see that an airline company can be the subject of verb fly the value of case slot argl when the direct object the value of ease slot arg2 is an airplane but not when it is an airline company NUM
we would also like to evaluate the usel ulness of partia l disambiguation decrease of ambiguity number of times correct sense is among the chosen ones etc
to compare figures better consider the results in table NUM were the coverage of our algorithm was easily extended using the version presented below increasing recall to NUM NUM
the experiments see figure NUM showed that there is not much difference adding meronymic information does not improve precision and raises coverage only NUM approximately
the method he uses has to overcome a combinatorial explosion NUM controlling the size of the window and freezing the senses for all the nouns preceding the noun to be disambiguated
conceptual density overcomes the combinatorial explosion extending the notion of conceptual distance from a pair of words to n words and therefore can yield more than one correct sense for a word
extracting word correspondences from bilingual corpora based on word co occurrence information
finally with respect to the mood system only substep can be realized through imperatives
an important defining property of a sublanguage is that of closure both lexieal and syntactic
figure NUM genre related differences in the modal system for goal
two sources of control over the generation of software instructions
our work focuses on the single application domain of software instructions
the declarative mood predominates with few imperatives addressing the reader
we coded here for features such as voice and agent types
sub steps actions which contribute to the execution of the plan
if all the variables of the applied pattern are instantiated or a substring can be matched to a pattern whose variables are all instantiated a passive arc is created for the substring
NUM 7bll somcone at the fl ont desk what game you want to scc and what type of seat you want and they ll get the tickets for you
average translation times in the bottom up method were NUM NUM se onds for a NUM word input and NUM NUM seconds for a NUM word inl ut
goes a m expresses the bindings for variables x and y where x goeg and y a m
the other is the combination of NUM and NUM where x at NUM is a verb phrase
noun verb creates the active arc NUM whereby the variable x of x noun verb f is matched against NUM
NUM if NUM fails choose the first item in the current sentence s cf list that is discourse old i.e. is already in the discourse model
in turkish items that are presentationally or contrastively focused are placed in the immediately preverbm ipv position and receive the primary accent of the phrase
the sentence planner uses the algorithms in the following subsections to determine the topic focus and ground from the given semantic representation md the discourse model
the translation proceeds sentence by sentence leaving aside questions of aggregation etc but contextual information is used during the incremental generation of the target text
the english text in NUM is translated using the word orders in NUM following the mgorithrns given above
the backward looking center cb is the most salient member of t he cf list that links the era rent utterance to the iwevious utterance
generalizing from his work we can determine whether an entity should be contrastively focused by seeing if we can construct an alternative set from the discourse model
this is the case for mental a special case of a highly reliable and more general indicator concrete see section NUM NUM concerning the more complex case of military entity and the relation of text type which we do use to the time period attribute
for example an old forest is not new if it has existed for a great period of time and not young if it is in an advanced stage of development in the life cycle of forests cf the discussion of wine in section NUM NUM NUM
in some cases such as hard fact it is difficult to draw the line between a noun specific sense here incontrovertible and a compositional sense e.g. inflexible unyielding such indeterminacy is of course one of the sources of a freeze
how to access the relevant noun sense is an unsolved problem the noun s direct referent is an individual whereas the semantic structure entailed by the noun is a semantic network and the adjective may apply to the network s noun sense nodes rather than to the noun referent itself
more generally a feature f such as the modified noun man that is associated with a target word t computational linguistics volume NUM number NUM such as the modifying adjective old is an indicator for a sense si e.g. aged of t if when the feature is present that sense si is more likely than the other senses sj j i
for example people shows a statistically significant tendency to be associated with the aged not young senses of old when people is the plural of person as judged from the co occurrence sentences although one instance of the of long standing not new senses of old when people meant ethnic group was also found
another way to confirm that our measurements of idf variance and h have consequences across years in the ap data is to note that measurements of idf variance and h in NUM can be used to predict word frequency in some other year
but this answer does n t explain the fundamental difference between boycott and somewhat boycott has an idf of log2676 d NUM NUM bits only a little more than somewhat which has an idf of log NUM NUM d NUM NUM
the remainder is constant across languages a language independent core and an optimally derived feature set for english
this data was hand tagged with the locations of companies persons locations dates and other
the new features are added to the tokenized training text and the process repeats
the feature which minimizes the weighted sum of this function across both child nodes resulting from a split is chosen
the approach taken here is to utilize a data driven knowledge acquisition strategy based on decision trees which uses contextual information
a decision tree is built based on the existing feature set and the specified level of context to be considered
al NUM matsumoto et al NUM attaches parts of speech
the system was first built for english and then ported to spanish and japanese
designators are features which alone provide strong evidence for or against a particular name type
as shown in section NUM some suffixes may disambiguate a certain number of words whereas others may be truly ambiguous and overlap over several categories of words
the verb jack up has no direct equivalent in french
carry out the renewal of hydraulic liquid
our corpus analysis reveals that a precise account of operator verbs is required
the dotted arrows indicate the possible lexical mappings of the conceptual predicate fill
consider the following pair 5e gain access to rear compartment
however operator verbs can not always be avoided even in english
complex world s 8e unlock valve clapper nut
which of these two versions can be considered more specific
null to deal with this lexical phenomenon two lexicalisation rules are involved
NUM describes similar divergences between english and german instructions
for unchangeable words and in certain other cases no real variation takes place and the set of variants contains a single element namely the given homonym
an additional condition was imposed that the initial word should belong to the set of variants of the new one sometimes it may not hold
on the whole for the first series of distorted sentences the corrector s reaction was right in NUM cases and for the second in NUM cases
in order to correct a sentence with such errors an extended morphological structure is created which contains various grammatical forms of the words used in the sentence
tim algorithms by which the corrc tor constructs the initial and extended morphss are similar to the algorithms of morphological analysis and synthesis used in the linguistic processor
c r l c NUM then for r r1 the minimal sets of fragments covering all words of thc sentcnec are considered
the process terminates after the stage at which complete syntss have arisen otherwise the fragments left after the third stage are regarded as the final result of parsing
at the second stage the fragments are connected with weaker and more ainbiguous links like those between a verb or noun and a modifying prepositional phrase
corrections were proposed in one case the time limit NUM seconds was exceeded one case gave an overflow of working arrays
for that reason all sentences whose degree of disconnectedness is less than that of the input sentence c are regarded as improvements
probabilistic and rule based tagger of an inflective languagea comparison
cases of incorrect tag assignment are in boldface
then the speaker modifies patterns by replacing subconstituents by expanding it with modifiers and by transforming it into different syntactic constructions for example transforming it from the declarative mood 4to the interrogative mood or from the active voice to the passive voice
nanka muzukasii yo ne souiu no hedge be difficult part part such thing kotowaru no tte reject thing top the propositional content of speaker b s response is to reject something like that is difficult but the utterance also contains a number of natural speech properties that add certain pragmatic elements of meaning
expressions of the speaker s hesitation or tentativeness such as hedges well i do n t know i think i am wondering if use of the interrogative form or the past tense i was wondering if or the subjunctive mood it would be better are examples of such devices to soften the force of the utterance and to make it easier for the addressee to refuse
for example softening the effect of an imperative force by questioning the addressee s ability to perform the action can you do x for me or asserting the speaker s desire i would like you to do x for me can be found across many languages
this is probably because the hand crafted dictionary was filtered manually which ensures that all of its concept nodes are relevant to the domain although not all are useful as classifiers
finally we evaluated the system by classifying two blind sets of NUM texts each the tst3 and tst4 test sets from the muc NUM corpus
the autoslog dictionary was generated using an annotated corpus and was subsequently filtered by a person so it relied on two levels of human effort
for example n NUM eliminated all concept nodes that were proposed exactly once and reduced the size of the dictionary from NUM NUM to NUM NUM
in contrast the autoslog ts dictionary was not filtered manually so the statistics are solely responsible for separating the relevant concept nodes from the irrelevant ones
the autoslog ts dictionary produced results comparable to a hand crafted dictionary on both test sets and even surpassed the precision scores of the hand crafted dictionary on tst4
NUM statistics are computed to determine how often each concept node was activated in relevant texts and how often it was activated in irrelevant texts
tions as described in the previous section autoslog requires an annotated training corpus in which the noun phrases that should be extracted have been tagged
for example x formed would presumably have a much lower relevancy rate than x formed venture in the joint ventures domain
after stage NUM we have a large set of concept node definitions that collectivelyl can extract virtually NUM every noun phrase in the corpus
the structural complexity of the center embedding sentence is NUM which is much higher than the structurm complexity NUM of the noncenter embedding sentence
np ntodifiers of a head tend to be closer to the head than its pp modifiers which in turn tend to be closer than its cp clausal moditiers
table NUM syntactic signatm e for change of state break subclass
the first distinction is whether or not to count the negative evidence
these results are shown in the three major rows of table NUM
NUM determine which semantic classes have uniquely null identifying syntactic signatures
in fact NUM of the verbs appear more than once
role of word sense disambiguation in lexical acquisition predicting semantics from syntactic cues
we are currently porting these results to new languages using online bilingual lexicons
each of these classes is characterized syntactically with a set of sentences
NUM the syntactic classification based on the derived syntactic signatures
in other cases tile verb may have largely unrelated senses
while planning and articulating utterances using an abstract domain plan a more concrete domain plan is being made
by applying these schemata to the first action in r8 the following utterance plan is obtained
a sequence of surface communicative actions corresponding to the uttering of linguistic ext ressions is finally planned
dc scribed obj o NUM domain object o is described s an object having content c
the decomposition method rt specifies how the mtion is decomposed to a sequence of finer actions
pragmatic constraints are exploited to guarantee the relevance of discourses which are evaluated by an utterance simulation experiment
NUM bottom i dependency parsing lu this sec taon
it misattachcd t hc fewest words both overall aud in each categol y
it is reasonable to expect a given move to be correct about as often on test data
is this one parser really compatible with all three probability models
table NUM verbs and their dependent slots
at this point the generic nautilus code ends and the system developer must hand craft an application specific interface layer between the translation functions and the target
theorem NUM the running time of algorithm NUM on input tree t is NUM i ti pt t log t t
tile preconditions of an action are tile proi osi ions that must hold before the action s successful execution
based on this idea we constructed a plan parsing method that handles the effects and precondi tions of actions
for a computer to participate in a dialogue like people do it must simulate such mental state changes
by applying h mrisii rules according to which a candi lat that has h im frequ ntly r pe m l in th NUM re eding sent m es and it candidate th tt modifi s the morl hoh gi a lly
this is reflected in the parsing operation descriptions given in section NUM NUM
the operations set contains the operations used to fill dp table cells
such a sequence of overparsing operations can be considered a overparsing cycle
overparsing operations consume no input they only add new unfilled structure
overparsing operations are discussed in section NUM
the other three constraints are faithfulness constraints
table for category m input i1 i3
to test the scalability of lexas we have gathered a corpus in which NUM NUM word occurrences have been manually tagged with senses from wordnet NUM NUM
we believe that our result is significant especially when the training data is noisy and the words are highly ambiguous with a large number of refined sense distinctions per word
however his work used decision list to perform classification in which only the single best disambiguating evidence that matched a target context is used
surrounding words give lower accuracy perhaps because in our work only the current sentence forms the surrounding context which averages about NUM words
this data set consists of NUM sentences each containing an occurrence of the noun interest or its plural form interests with its correct sense manually tagged
note that in bruce and wiebe s test run the proportion of sentences in each sense in the test set is approximately equal to their proportion in the whole data set
of course these complexities extend to the liged forest l z
the reason for the renewed interest in finite state mechanisms is clear
again the advantages of representing the rules as a transducer outweigh the problems of size
branch or one or more for the right branch
table NUM arpabet ipa conversion for symbols relevant for figure NUM
we report on a method for compiling decision trees into weighted finite state transducers
NUM the rule input c has already been given as aa
the ruleset for a for example contains NUM ordered rewrite rules
null we have acquired about NUM NUM of our lexicon semi automatically and have developed a morpho semantic acquisition program which has allowed us to acquire the remaining NUM NUM entirely automatically to create at the end a large scale lexicon of about NUM word senses NUM the main advantage of our approach is that it enabled us to economically multiply the size of the lexicon
the distribution of brand new the starred line of the table versus discourse old information the rest of the table NUM is statistically significant x NUM NUM NUM
verbs can be in the focus or the ground in turkish this can not be seen in the word order but it is distinguished by sentential stress for narrow focus readings
i argue for a more generative approach a particular information structure is can be determined from the contextual information and then can be used to generate the felicitous word order
however it is possible to have a disconrse new topic and a discourse old focus as wc will see in the following sections which explains the exceptions to the old to new ordering principle
thus the algorithm needs to be extended to a comnaodate discourse new verbs that are nonetheless expected in some way into the ground component
however as can be seen in figure NUM most of the focused subjects in the osv sentences in my corpus were actually discourse old information
multiset ccg was developed in order to capture formal and descriptive properties of free and restricted word order in simple and complex sentences with discontinuous constituents and long distance dependencies
this component consists of a head driven bottom up generation algorithm that uses the semantic as well as the information strncture features given by the planner to choose an appropriate head in the lexicon
they use the referential form and repeated mention of items in the english text in order to predict the salience of discourse entities and order the polish sentence according to this salience ranking
at the deep syntactic level only arguments agent and location will be realized as actants of the verb unlock agent as actant and location as actant ii
using the information provided by the unamt iguous NUM rel ositional phrase in the flow of a job in sentence NUM
this problem has already been tackled in NUM NUM
in english rt should be privileged and r2 applied only if rx fails
r2 operator verb construction NUM look for a mapping structure p n
recently some works in the fields of machine translation and computational lexicography e.g.
ghostwriter is a bilingual generation system under development at dassault aviation and british aerospace
this paper describes ongoing research on the lexicalisation problem in a multilingual generation framework
we will see that the writing rules defining these languages are sometimes too general
let us start with the first type of differences domain specific us ordinary verb
this rule can be used as a guiding principle in the verb selection mechanisms
selecting a more specific verb does not necessarily lead to a more specific instruction
however we have chosen to stipulate it here since although the spelling appears regular the phonology is not so in a lexicon that defined phonological forms it would need to be stipulated
clearly if we want to specify other forms at the same level of generality then mor form is currently misnamed it should be mor present participle so that we can add mor past participle mor present tense etc
and to disconnect requires a direct object the entity that is disconnected and a source the entity that something is disconnected from which can be omitted if it is obvious from the context disconnect tile wire
our interfaces have been designed with respect to users needs and continue to evolve on a needed basis
these tools range in complexity from checking placement of parentheses to automatically creating sentences to test individual lexicon entries
reversibility of the lexicons the way we organised and structured our lexicons directly follows these conditions
agent NUM human theme object figure NUM partial entry for the concept acquire
many thousands of such entries have been created tools such as this provide a simple way to check their accuracy
we have developed a method that enables us to take advantage of the large investment made in the mikrokosmos analysis lexicon
the main drawback is that the en null using the reversed lexicon to regenerate the entry as explained below
the sub entries for adquirir have different selectional restrictions for the theme object and information for acquire and learn respectively
thirdly the example presupposes a distinction between the syntactic arguments list syn args associated with a lexeme and the subcategorization frame list syn subcat associated with a particular syntactic form of a lexeme
furthermore since our system takes lexicalization as the decisive task in mapping a sitspec to a semspec the um concepts referred to in a semspec must be annotated with lex expressions thus a semspec is a lexicalized structure
the paper provides an informal example based introduction to datr and to techniques for its use including finite state transduction the encoding of da gs and lexical rules and the representation of ambiguity and alternation
the described methods have been implemented and successfully tested on six languages
NUM implementation and performance of the part of speech tagger we have developed a part of speech tagger using only a finite state machine framework
fatality was victim bomb against target killed with instrument was aimed at target to fir at victim figure NUM autoslog heuristics and examples from the terrorism domain
our goal is to do without the primitive and to define the change in terms of the aktionsart of the verb to this end we use resulta wive in the place of inchoative see section NUM NUM
the former cir umstances of disposal mid ownershi t so and st abut on ee so zxz e c sl
109f secondly the thematic roles are specified individually for each lexical entry there in no get eralization with respect to lexical fields
to nmke the description of leihen complete a further lexical axiom which explicitly notes the belief in a return of the involved object is ne6ded
elinan vei l leihen intt lies the lending t crson s belief in a l eturn of the involved object
because of the temporal identity of e21 and e2 there are temporal overlaps between the initial states as well as between the final states
given a basic semantic form bsf as a cornmon starting point we derive semantic and syntactic case frames and construct prototypical meaning descriptions of concrete lexemes by refining the bsf
from both the syntactic and the semantic point of view the bsf delivers the maximum case frame of the lexemes that constitute the lexical field
t redieate bec i1 has one mgmnent whi h is t redieate a rgunw nl strucl ur
therefore the transitions initial states precede ec so NUM ee and the transitions final states follow ec ec dc st
l he a rchitecture fits multi moda NUM systems whose multi modm expres sions a re sophistica ted
level NUM a colnbination of mode inputs still lacks something the contents genera ted
mm dcg is a superset of NUM NUM pereh a
multi moda l NUM cg mm i cg supports these functiona lities
l ut modes include l ictures and text but outputs axe llot synergistic
figure NUM shows a part f the grammar rules written in mm i cg
such ambiguous pointing can be orre t y interprete d
a trivial heuristic rule exmnple is to use the tost recently a ppea red
in typical cases speakers motivate addressees to adopt an action by asserting that its precondition is satisfied
o e thematic relation it is described which domain object o bears to domain event e
one is based on the fact that negated event sentences accept durative complements whereas their positive counterpart do not this fact being taken as an argument in favor of the aspectual role of negation
thus if such a representation is sufficient to account for all the data we want to account for then there is no need for a more complex representation like tile one exemplified in k b
this requires the definition of linguistically grounded semanl ic and synl acl i r NUM res ml al ions
to extract the latter we are describing a set of rules or patterns
as mentioned in section NUM the co occurrence sets of a word are accumulated
a new method has been developed for extracting word correspondences from a bilingual corpus
these proposed methods for extracting word correspondences from bilingual corpora have the following drawbacks
second the statistical methods usually require a very large corpus as their input
therefore we extract only the content words from the texts in both languages
parameter a in the selection of pairs of words was assumed to be NUM
the dictionary contains approximately NUM NUM japanese entry words each having several english translations
generally speaking the wider the coverage the more reliable the correlation values
the english based and japanese based approximate calculations therefore do not always coincide with each other
removing the direct or indirect cycles from the magic part of the compiled grammar does eliminate the necessity of subsumption checking in many cases
through reordering the right hand sides of the rules in the grammar the amount of nondeterminism can be drastically reduced as shown in minnen et al
b if no synonyms in levin or canonical ldoce codes are completely mismatched hypothesize new class
we will see that our classitication technique shows a NUM fold improvement in the experiment where we implicitly account for word sense distinctions
finally we show that we can provide effective acquisition techniques for novel word senses using a combination of online sources
to the extent that these functions map between syntax and semantics intensionally they will pick out the same verbs extensionally
the NUM sets of sentences listed with each of the NUM semantic classes in turn reduces to NUM distinct syntactic signatures
conceptually it is helpful to consider the difference between the intension of a function versus its extension
thus an overlap index of NUM NUM is a complete overlap and an overlap of NUM is completely disjoint
the core syntactic grammar of about NUM context free rules and NUM restriction rules developed for eucalyptus has been re used in all the other nautilus projects
unlike the nautilus grammar the speech grammar excludes iteration and recursion such as compounding to maintain a reasonable level of recognition accuracy
it is easy to see that the number of pairs totmly instantiated by the algorithm is also a bound on the number of indices inserted in or retrieved from the heap
a set rule i is associated with each rule ri containing some of the nodes of the rewritten input tree at which lhs ri matches
the body of the for statement in the finally index NUM is retrieved from h and node m27 is again considered this time for the application of rule r3
the height of t is the length of a longest path from the root to one of its leaves a tree composed of a single node has height zero
the discourse structure was analyzed in terms of information units and discourse relations
after such an action has been described NUM is in the focus
thematic relations are chosen in default order when r12 is applied
figure NUM model overview terence plan obtained within the time limit
based on a refined domain plan the utterance plan is replanned
pragmatic constraints and a context model are used to generate relevant discourses
this paper is concerned with a computational model of incremental utterance production
discourse relations between adjacent discourse segments w re examined
elaboration enables speakers to distribute the content to be conveyed among different lus
this paper presents a comtmtational model of incremental utterance production in task oriented dialogues
where llc is the left lexical context lex is the lexical form rlc is the right lexical context lsc is the left surface context surf is the surface form and rsc is the right surface context
i1 NUM handles measm e NUM it represents the operation o prefix it and the rule a left by placing b i in llc and the residue b in 11lc and inserting a consonant c representing t on the surface
npc o o b b o o b i in other words in ppc o applies to the kernel b concatenating the result with the residue b o in npc o applies to the residue b o concatenating the result with the kernel b o
the numbers between the two levels indicaw the rule mlmlmrs in NUM which sanction the sequences
the operator states that lex may surface as surf in the given context while the operator adds the condition that when lex appears in the given context then the surface description must satisfy sur
a lexical string maps to a surface string iff NUM they call be partitioned into pairs of lexical surface subsequences where each pair is licenced by a rule and NUM no partition violates an obligatory rule
the two level analysis of the cited forms appears below st sm face tape pt pattern tape NUM root tape vt vocal lcb sin tat e and
to illustrate this let b katab applying the function o al left on b factors it into i the kernel b ka and ii the residue 1other conventions associate consonant melodies left to right to the morale nodes followed by associating vowel melodies to syllable initial morae
arguably this is an example of a particularly well behaved text in any case it is not clear how the figure would be normalized over a wide range of text types some of them not completely clean as is the case with our data
both the pronoun and the candidate appear in the same subordinate context within a relative clause as a result the salience of the candidate but not of the class to which it bekmgs is temporarily boosted to negate the effect of subordinatkm
the larger context from which the sample analysis in the beginning of section NUM was taken is as follows while apple and its powerpc partners claimed some prime real estate on the show floor apple s most interesting offerings debuted behind the scenes
their unification into a single class indicates both successful anaphora resolution of the pronoun at offset NUM as well as the operation of higherqevel discourse processing designed to identify all references to a particular coref class not just the anaphoric ones cf
first the equality in salience weights of the candidates at offsets NUM NUM and NUM is a consequence of 2note that our syntactic filters are quite capable of discarding a number of configurationally inappropriate antecedents which appear to satisfy the precedence relation
the data set on which the evaluatkm was based consisted of NUM texts taken from a random selection of genres including press releases product annotmcemeats news stories magazine articles and other documents existing as world wide web pages
instead with minimal compromise in output quality the modifications enable the resolution process to work from tile output of a part of speech tagge enriched only with annotations of grammatica functkm of lexical items in the input text stream
we consider that such successive ees involve the same context that is introduced by several successive short sentences
moreover our assumption states that when non prr anaphors have intrasentential antecedents they occur in embedded sentences
moreover sidner s approach does not impose its own formalisms syntactic or semantic for its application
step NUM perform the basic focusing cycle for every anaphor of all the ees of the current sentence
indeed an important assumption we have made is that embedded sentences favor the occurrence of intrasentential antecedents
the algorithm is designed for anaphors generally even if we focus mainly on pronouns in this paper
indeed the distinction between different kinds of anaphors is made at the level of anaphor interpretation rules
null c the result is a set of readings rij sj eej sfi
the addition of decision trees at each state of the resulting transducer further improves accuracy and results in phonologically more natural transducers
the decision trees classify the arcs leaving each state based on the arc s input symbol into groups with the same behavior
there is no guarantee however that the employed smoothing method is in any way consistent with the clustering method used subsequently
in contrast a model with more clusters is more complex but tends to have a better fit to the data
for instance the speed of convergence of the models selected by mdl to the true model is known to be near optiinal
however since the number of probal ilistic models under consideration is super exponential this is not feasible in practice
iii repeat step ii until some stopping condition is met to construct a thesaurus tree
will result in selecting a very fine model with many small clusters most of which will have probabilities estimated as zero
its formalization comprises two parts which are conceptually independent
we have recently implemented a basic version of this tagger initially incorporating only the part of speech NUM NUM and dictionary definition NUM NUM stages in the process with further stages to be added later
qbny broke the cup against the wall
system sin most languag s have orre sl onding an q hori expressions tnd us of th corre sl onding a naphori expression in lhe translation oull ut hi s the advilnt tge of a voi ling misint rl r ta tions
qbny broke herself on the arm
at the same time for the reasons already mentioned there are advantages in being able to provide the information requested by the user in a dynamic fashion
however applying nlg to hypertext allows the possibility of dynamic hypertext where the document is created as demanded by the user
while there are quite restricted directions that the conversation can take each party can take the initiative in pursuing a direction
within the last ten years there has been a upsurge of interest in hypertext as a medium for on line access to written documents
this is an increase in functionality compared to existing static museum hypertexts for instance as created in the uk by the hunterian museum and the national gallery
indeed flexibility is not without cost for instance canned text can offer fluency which the most advanced nlg systems today have yet to reach
in general such concatenated structures will be ordered lists of trees
ot uses optimization to define grammaticality
model NUM was used for this test on all sentences NUM words in section NUM
we assume that we do not use any restriction i.e. for any lexical entry l and rule schenaata NUM s bb NUM NUM and sub fs i lcb NUM
in other words it removes the feature structures that have i een already raised to core structures or other dfss ex ept for the structure sharings and leaves those which will be required by dcps or xub fs r
treats sub fs r a feature structure eliminated l y the restriction in the generation of las the a NUM art in figure NUM and frozen goals of dcps by additional ewduation of dcps
in the first l hase our NUM arser produces parse trees using lexical entry automnta compilcxl from lexical entries in the second phase only the feature structures whi h luust e ompute dynamically are omputed
the result of execution in a prolog like sense appears in the query figure NUM is an example of execution for the query append a b x whose definition is based on a standard definition of append in prolog
to further refine the selection process we have to take into account not only the types but also the specific values of the data samples
since one of our goals was the study of the integration of text and graphics we decided to always generate a text graphics pair for every message
they are also used for ordering variables in some graphics so that the more important ones usually the keys are given the more visible positions
this problem is often complicated by the fact that a single graph or text can NUM convey many messages at once some more direct than others
ks we have shown in this paper all of these factors must be taken into account simultaneously in order to produce an efficient report
unfortunately too many high level choices depend on simple low level details such as the number of available colors or the positioning of textual labels in a graph
the advantage of such an approach is that the table could be automatically modified by the system in response to user satisfaction or dissatisfaction with a result
the algorithm does n t try to maximize the overall efficiency of a result but assumes that important variables are listed first and gives them the best encodings
the tdmt system is being expanded so as to handle travel arrangement dialogues including the topics of hotel reservation room services troubleshooting during hotel stays various information queries and various travel arrangements
the influence of ordering appears to be at the level of weak preferences in line with portuguese rather than the stronger role seen in french generation
tation that discourse cues are significantly more likely to occur when the contributor component of the relation precedes the core in english dialogues
clearly this yields an iconic ordering in the cause of apt and a non iconic ordering in the case of avant
the imperative simple on the other hand is identical in form to the second person plural NUM indicative of the verb and its use is associated with identifiable addressees
for generation the dominant relation is purpose NUM for enablement it is sequence NUM
portuguese appears to have obligatory signalling of discourse relation by a discourse marker or at the very least by punctuation s
in order to evaluate the relative contribution of the knowledge sources including NUM pos and mor
when a bare adjunct phrase adjp does not correspond to a phrase in the antecedent clause adjp is simply added to the list of adjuncts of the new head verb of the reconstructed clause
these are words that are not morphologically inflected such as interest as opposed to the plural form interests fall as opposed to the other inflected forms like fell fallen falling falls etc
however the pos used are abbreviated pos and only in a window of b2 words
in this paper we have presented a new approach for wsd using an exemplar based learning algorithm
the classification accuracy of lexas is always better than the default strategy of picking the most frequent sense
the recent emphasis on corpus based nlp has resulted in much work on wsd of unconstrained real world texts
the accuracy on brown corpus test files is lower than that achieved on the wall street journal test files primarily because the brown corpus consists of texts from a wide variety of genres including newspaper reports newspaper editorial biblical passages science and mathematics articles general fiction romance story humor etc
we shall sometimes refer to descriptor sequences containing only atoms as simple values and similarly unquoted path expressions containing only atoms as simple paths
NUM for simplicity here we consider only the case of descriptor sequences of length one the general case involves complications not relevant to the main point
tm of course to be effective this filling in has to take place before the operation of the inheritance mechanisms described in the previous section
we fully expect our results to improve with the addition of further independent modules
b take the head verb of s a as the new interpreted head of the sentence to be constructed from s we will refer to the new head as a
NUM reorder the elements of ph list to produce a new list ord ph list in which the sequence of arguments and adjunct phrases corresponds to the order of arguments and adjuncts phrases of a
we refer the reader back to figure NUM and the under constrained cases of modality in goal polarity in constraint and mood in substep
as part of the realisation process generic choices preselect a register associated with particular elements of text structure which in turn preselect lexico grammatical features
in the light of these results we considered a corpus of NUM words to be adequate for our purposes at least for an initial analysis
1we would have preferred to use a manual which originated in french to exclude all possibility of interference from a source language but this proved impossible
these figures show that genre does indeed provide useful additional control over the expression of task elements which can be exploited by a text generation system
NUM the first step at this stage of the analysis was to establish whether there was an effective overlap in task elements among the three genres under consideration
the coding of our text in terms genre and task elements thus allows us to establish the role played by genre in the realisations of the task elements
the overlap in information between the two chapters offers opportunities to observe differences in the linguistic expressions of the same task structure elements in different contexts
we used the notion of task structure element both as a contextual feature for the analysis and to determine the segmentation of the text into units
disagreements in the value of a semantic attribute for a given noun can even be systematic
the utility and elegance of such semantic representations is suggested by linguistic discussions on lexical semantics
later mama may have regretted being married because papa was so hard to understand
such verbs relate to the alternative sense only when they are outside of the infinitival constructions
the characterization of indicators is equally flexible with regard to the domain of its own applicability
the attribute color disambiguates about half of the not dark instances of light
he saw the tractors come and tear down the old houses and plow up the land
two of the nouns most frequently modified by old in general texts are man and house
superficially this might support the pertinence of a special attribute textual for disambiguating short
there is therefore every reason to believe that coverage would increase with a larger base for inference
NUM for resolving an anaphor in a subsequent clause of u a propose already context bound elements of un from left to right NUM
this might constitute a cognitively valid argument for the functional approach the better the strategy the lower the influence of semantics or world knowledge on anaphora resolution
in sentence NUM another nominal anaphor appears der rechner the computer which is resolved to t3100sx from the previous sentence
for NUM of the errors it chooses an inter sentential antecedent which is on the surface identical to the correct intra sentential antecedent
in the matrix clause the pronoun er it co specifies the already resolved anaphor der rechner in the subordinate clause
in this paper we gave a specification for handling complex sentences in the centering model based on the functional information structure of utterances in discourse
since intra sentential anaphora occur at high rates in realworld texts the model has to be extended for the resolution of anaphora at the sentence level
we acquired fronl the saint corllus
table NUM sample of target texts
thresholding the mutual information extracts fixed collocations
colnol degont adopt dance j p
this level of performance was achieved in the face of two problems
these collocation are exl l enlc ly
this is indeed shown to be the case since the pp for email is NUM NUM homogeneity NUM NUM whereas the pp for the bnc is NUM NUM homogeneity NUM NUM
would be the use of the subjunctive mood could you do x for me but no corresponding form exists in japanese
table NUM types of intra sentential antecedents
the problem we face in learning to convert ibm lancaster treebank parses into atl lancaster treebank parses is rather more difficult than this
processing complex sentences in the centering framework
table NUM evaluation results with varying number of
balanced by rich context and some background knowledge our corpus based approach relieves the nl developer from the hard if not impossible task of writing explicit grammar rules and keeps grammar coverage increases very manageable
we expect our feature set to grow to eventually about NUM features when scaling up further within the wall street journal domain and quite possibly to a higher number when expanding into new domains
the resulting translations in randomized order and without identification were evaluated by ten bilingual graduate students both native german speakers living in the u s and native english speakers teaching college level german
this observation further supports our expectation based on the results shown in table NUM and figure NUM that with more training sentences the testing accuracy for unseen sentences will still rise significantly
this is very important because during the training phase the system is guided by a human supervisor for whom the flow of control needs to be as transparent and intuitive as possible
table NUM evaluation results with varying types of
in order to better assess the contribution of the parser we also added a version that let our system start with the correct parse effectively just testing the transfer and generation module
the generated parser transforms input sentences into an integrated phrase structure and case frame tree powerful enough to be fed into a transfer and a generation module to complete the full process of machine translation
focusing discourse new information is often called presentational or informational focus as shown in NUM a
we have discussed a mechanism for building dialogue systems and how one might achieve useful behaviors such as handling subdialogues allowing variable initiative accounting for user differences correcting for errors and communicating in a variety of modes
this research effort will focus on understanding and formalizing these communication languages so that they are not only powerful enough to capture the dialogue information we can obtain today but also extensible enough to convey novel pieces of the human machine interaction allowed by future developments
speech input such as the voltage is six volts was translated to predicated form such as answer measure NUM NUM t202 NUM and turned over to the theorem proving mechanism
the appro tch described in section NUM can bc i nplemented in any environment that supports typed inheritmme because it is monotonic and demands only trivial inference power
we have shown that this is an easily implementable proposal even in environments with lean inference power and expressivity because it relies on very basic machinery which is available for independent reasons
the grammar must have access to three different ver null sions of to load one with zero pp complements and two with a pp complement participating in the alternation discussed above
these include the so called spray load locative mtornation the wipe clear a lternation the b vak hit alternation etc l evin NUM a
figure NUM tree modeling the phonetic realization of aa
the first training tests for the co te and st module were conducted as soon as the system was able t o process all the testing articles without crashing
the third component matches a sequence o f zero or more modifying word for money such as new taiwan canadian etc
NUM a xyz of canada c the boston ma company b the boston based company d the computer maker headquartered in boston e xyz inc
the st module is executed after all the sentences in the text have been parsed all the coreference relationships has been asserted and all the template elements have been created
the pie system on the other hand relies heavily on a principle base d broad coverage parser called principar NUM NUM NUM that we have developed over the past three years
some proper noun entries have semantic features to indicate whether they are company names compan y name designators locations family or given names etc sample entries are listed in NUM
ca background the pie principar driven information extraction system takes a different approach to the problem o f information extraction from the nuba system that was used in muc NUM
john smith its next chairman and abc are the n recognized as the holder post and organization of an in event
for example the locale of an organization is identified by finding th e closest indirect dependent of the organization that has the semantic feature location unless there is an intervening organization node
performance in muc NUM considering the very limited time and human resources with which the pie system was developed it di d very well in all of the four evaluation tasks especially in coreference recognition
sw t close to NUM indicates neutral evidence which is of little or no consequeuce to the spotter
null the entities we rare looking for inay be exl ressed y certain tyt es of phrases
fhcre mrc two itnportant difl erenc s however
tile results shown here are promising can be further improved by using lexicon verification
the results for products tagging are given in figure NUM on the next page
this makes treat ion and extension of stleh spotters an arduous mamml job
the user provides the initial information seed about what kind of things he wishes to identify in text
in fact is can be used to create more or less on demand spotters depending upon the applications and its subject domain
the experiment shows that this method indeed can produce useflfl spotters based on easy to construct seeds
both x and y are positive and it is less than both x and y for negative x and y
it is therefore interesting that the covariation lexical rule compiler produces a lexicon encoding that basically uses an underspecification representation the resulting definite clause representation after constraint propagation represents the common information in the base lexical entry and uses a definite clause attachment to encode the different specializations
since the running example of this paper was kept small for expository reasons by only including features that do get changed by one of the lexical rules which violates the empirical observation mentioned above the full set of lexical rules would not provide a good example
summing up the relation between parsing times with the expanded out exp the covariation cov and the constraint propagated covariation imp lexicon for the test grammar can be represented as imp exp cov NUM NUM NUM NUM
this excludes lexical NUM the reason for first determining the automaton on the basis of the follow relation alone instead of taking propagation of specifications into account right from the start is that the follow relations allow a very simple construction of a finite state automaton representing lexical rule interaction
computational linguistics volume NUM number NUM simple word le1 v v len derived word in lrl in a lrl out v v in lrm in a lrm out figure NUM the extended lexicon under the dlr approach
the encoding consists of three types of definite clause predicates NUM lexical rule predicates representing the lexical rules NUM frame predicates specifying the frame for the lexical rule predicates and NUM interaction predicates encoding lexical rule interaction for the natural classes of lexical entries in the lexicon
the space efficiency is dependent on the size of the lexical entries since in the covariation encoding much of the lexical information that is specified in a base lexical entry is not duplicated in the lexical entries that can be derived from it as is the case for an expanded lexicon
the interpretations were ranked by the syntactic likelihood psy and the interpretation of attaching the off phrase to fertilizer was mistakenly preferred
our approach requires some lexical semantic information to identify possible inputs to rules but the need for detailed definitions of narrow classes for which rules can be treated as fully productive is reduced since failure to identify a narrow class will lead to less accurate prediction of probabilities rather than over generation as is the requirement to identify synonyms to predict blocking
to resolve anaphors one of the most suitable existing approaches when dealing with anaphor issues in a conceptual analysis process is the focusing approach proposed by sidner
figure NUM decision tree before pruning the initial state
based on a number of independent motivations we have adopted a hybrid analogical approach to the problem of translating spoken language
a direct one to one mapping of each pragmatic strategy operator to the target language is not possible since many of these operators are not directly translatable to other languages
the use of the first person plural pronoun we in english is also an example of solidarity politeness
as pawley and syder note these memorized sequences have varying degrees of lexicalization
the majority of utterance strategies express the speaker s interpersonal intentions the main aspect being politeness
the volitional aspect of politeness is usually ex pressed through projection of face
we refer to all the devices that serves pragmatic or communicative functions as utterance strategies
information this section discusses the different types of pragmatic information that play a role in spoken dialogues
we have proposed a mathematically well founded method to compute probability distrii ution in which a string belongs to given poss
d et zp posk a d posk NUM k
NUM tal e ru to eat NUM i type adjective e.g.
first we calculated f mi and p for all character n grams means unknown word
in calculation of the recalls and the precisions both pos and string is distinguished
the reason for this is that the amount of the output is too enormous to check by hand
NUM divide each vector element by the total number of o currences of the pos
the definition of structural complexity presumes the notion of dependency relationships between words in a sentence
intuitive y certain syntactic structures arc ulore difficult for htnnans to process thau others
this explanation presumes that the human parser uses a push down stack to store the partially built constituents
only the dependency links whose lengths are changed are shown there
does i deed retlect the difficulty in processing a sentence
gennan center embedding structural complcxily NUM the men taught hans to feed the horses c
null secondly structural complexity is also needed in assessing the readability of dommtents
we identified the following two modes of integration i extraction to detection broad coverage extraction NUM extraction step identify concepts for indexing NUM detection step NUM low recall high initial precision NUM detection step NUM automatic relevance feedback using top n retrieved documents to regain recall
at the same time there are numerous phrases such as insider trading case insider trading legislation etc where the pair insider trading remains stable while the other elements get changed and significantly fewer cases where say trading case is constant and the other words change
the average precision NUM point has increased from NUM smart baseline on the test sample to NUM
when the first results are returned using boolean will place you in editor mode similar to run
the goal was to determine if advanced information extraction methods can improve recall and precision of document detection
database blackboards are hooked up to the discourse blackboard to make the representations more specific
finally as shown in section NUM using an expanded out lexicon can be less time and space efficient than using a lexicon encoding that makes computational use of generalizations over lexical information as for example the covariation encoding
NUM for example solely on the basis of the follow relation we are not able to discover the fact that upon the successive application of lexical rules NUM and NUM neither lexical rule NUM nor NUM can be applied again
the first step consists in typing the leaves of the tree while keeping track of the types of fl ee variables as in fig NUM
be cause john teleis to an individual not a quantified np these six pernmtations really corrcsl ond to only the two interl relaiiens given ahove
the s form is more compact and makes the dependency relations more conspicuous whereas the b form makes the compositionality more explicit
applying this encoding to our example we obtain the binary tree of fig NUM which is called a b form
for completeness detemainer incorporation which does not inw lve vmiable binding is given along with complement and rnodifier incorporation
cle ly the encoding called the sform b fi rm encoding which has just been defined is reversible
dislikes every woman thai peter hates are not distinguished by tile t l l oi nl of l ig NUM
montague gl anlmar hits the general requirement that only closed lambda tetms lanibda terms containing only bound variables are composed together
context conditions are defined in relation to the target reading which has the default position NUM positive integers refer to the number of words to the right and the negative ones to the left
null the most common types of ambiguity still remaining are noun vs adverb adjective vs adverb noun vs conjunction verb imperative vs noun and verb infinitive vs noun
if there is a need to define further conditions for a reading found by scanning by using position markers NUM or NUM the linking mechanism may be used
whereas in table NUM a corpus of running text corpus NUM was used in table NUM the source text was a list of unique word forms corpus NUM
in the first entry mu is the lexical representation of a morpheme and m mir is the name of the sub lexicon where the processing will continue
this is due to the fact that in addition to ambiguity found in several of the most common words verb forms are typically ambiguous as are almost half of the nouns
a parser wilt only grammar based rules disambiguatcs m out NUM or swahili wordtbrms from running text which initimly has about NUM of the tokens ambiguous
accordingly the filler of the obj ect slot in the occurrences here cl which instantiates the move transmission template is dways a symbolic label c2 which refers to anolher predicative cx curreuce i.e. that bearing the informational content to be spread out mediobanca had called a meeting
a stop list of NUM function words was therefore applied in step NUM NUM of the above algorithm
this general approach handles the possibility of specialized subcases of more general rules
measures of bilingual class class association and bilingual class frame association are introduced and used for discovering sense clusters in the sense distribution of english predicates and japanese case element nouns
lexical association has its limits however since often either the data is insufficient to provide reliable word word correspondences or the task requires more abstraction than word word correspondences permit
indicates the ratio of the total number of clusters to the number of hand classified senses correspoading to the average number of clnsters into which one hand classified sense is divided
NUM pairs of an english class and a japanese case class frame are found and the set of the NUM examples are divided into NUM disjoint clusters table NUM
liven a verb v e NUM and a norm class c c n the joint probability of v and c is estimated as
even if there exists exactly one case marker which is most effective for sense classification it is necessary to select the most effective case marker automatically by some measure
n the field of statistical analysis of natural language data the measure of word class association has proved to be quite useful for discovering a meaningtiff sense cluster in an arbitrary level of the thesaurus
in the field of statistical analysis of natural language data it is common to use measures of lexical association such as the information theoretic measure of mutual information to extract useful relationships between words e.g.
rooth s alternative semantics and krifka s struc
thus in 5a the quantification of only in the second clause is identified with the fsv of the preceding utterance i.e. the set of properties of the brm like ing somebody
l lcb enohiliion of the tirsi tuat ion ymds i hc followiug sohltion a jw l lcb x l
for each rule tri for each speaker j for each text k compare cik with hjk and note down the number of matches of anaphors between them
this average matching rate however is lower than the matching rates about NUM we obtained in the empirical studies described pre
after the annotations were collected we carried out comparisons between the speakers results and the generated texts to investigate the performance of the test rules
tr2 s text differs from tr3 s in the three topic shifts tr2 generates zero anaphors for these shifts while tr3 generates full descriptions
the measure of agreement gets worse if only the zero pronoun nominal distinction is considered or if zero and non zero pronouns are lumped together
as described previously the systems to compare in this evaluation work are assumed to share the same individual components except the anaphor generation rules
the comparison results suggest that a chinese natural language generation system employing the combination of these constraints might produce more effective anaphors than one using individual constraints
furthermore there are three topic shifts occurring in text NUM i.e. clauses NUM and NUM NUM and NUM and NUM and NUM
the overall kappa value for all speakers is about NUM NUM whereas a value of NUM NUM or over would normally be required for good evidence of agreement
we took the average number of anaphors matching between the machine and human texts as a measure of the quality of anaphors generated by the test systems
utterance strategies range from grammatically encoded information to extra linguistic devices such as facial expressions and body language to natural speech properties figure NUM
root with um gloss alis um alis leave sulat s um ulat write gradwet gr um adwet graduate mccarthy and prince analyze um as a prefix which moves into a word to reduce the number of coda consonants
marks are not added together rather the count of each type of mark is the deciding factor in evaluation s the output of gen and the constraints of eval are combined into a single transducer by taking the product of all of the fsts
NUM we removed those examples that although they contained the desired lexical string did not constitute negative imperatives
if you want to save the waste stock for later use drill the hole near a corner in the pattern
when the probe returned more than NUM examples for a grammatical form we randomly selected around NUM of those returned
as a collection these texts are the result of a variety of authors working in a variety of instructional contexts
this table shows the frequencies of examples marked as conscious or unconscious in relation to those marked as dont and neg tc
the number of examples that resulted is shown in row NUM of table NUM labeled raw sample
only recently have studies been making use of more rigorous statistical measures of accuracy and reproducibility used here
table NUM lists the x NUM statistic and its related level of significance for each of the features
pruning is done by stepping through each state of the machine and pruning as many decision nodes as possible at each state
topic empathy subject object2 object others
nodes rejected by a restriction are not added to the active chart and so do not contribute to the remainder of the parse
the antecedent c command the reflexive but in NUM a
a maria fhlou acerca do novo director com o pedro
x prep y si pr6prioi obl y
NUM l gta kniga byla kuplena borisomi dlja sehjai
a maria falou com o pedro acerca do novo director
in all examples the binding of the reflexive is ungrammatical NUM
in this solution the binding theory is kept invariant
this does not change the semantics of the original grammar as it merely serves as a way to incorporate the relevant bindings derived with the magic predicates to avoid redundant applications of a rule
the true assertion 2a violates the princit le of relevance in that it does not yie d
this NUM eok nom was bought boris instr tbr self this book was bought by boris lcb br himself rcb NUM the subject oriented behavior of sebe in active sentences results like in other languages with subject oriented reflexives from the non linear ordering of the elements of arg s value with all argi NUM i n being preceded by art1
computational linguistic the main advantage of this approach is that it is usually theoretically grounded and is domainand application independent
clauses in the corpus must be hand coded for transitivity
i a patient is wholly or partially affected e.g.
there is also potential for producing a transitivity index for an entire document as well as for individual clauses so that this measure could also feature in a relevance assessment
in automatic summarising it means that less important information could be sieved according to transitivity leaving only the most important information to form the basis of a summary
proper common human animate inanimate concrete abstract singular plural count mass referential definite non referential based on these components clauses can be classified as more or less transitive
the selection of sentences for a summary will be based initially on comparative transitivity scores and a reduction factor which will determine the number of sentences selected based on the length of a document
the proposal for this study is to code matching query document terms for the transitivity value of the clause in which they occur as a starting point for producing comparative term weights based on linguistic features
the results of the queries are downloaded in their initial ranked order ranked by a host ranking algorithm and re ranked by a serial batch processor written in c t
the notion of transitivity clearly has many implications for text processing in particular information retrieval and automatic summarising because it can be used to grade information in a document according to importance
each set of articles is ranked by volunteers
ping labels on arcs are of the form input symbol output symbol
figure NUM results on three rules composed NUM NUM training NUM NUM test figure NUM shows the final transducer induced from this
we hope that our hybrid model will be more successful at learning long distance dependencies than the simple decision tree approach
figure NUM onward tree transducer for bat batter and band with flapping applied
in our second experiment we applied our learning algorithm to a more difficult problem inducing multiple rules at once
the learned rules are represented as finite state transducers that accept underlying forms as input and generate surface forms as output
we then augmented ostia with two kinds of knowledge specific to natural language phonology biases from universal grammar
interestingly preventing the wrong states from merging early on allows more merging later and results in more compact transducers
this paper presents a method for learning phonological rules from sample pairs of underlying and surface forms without negative evidence
for example the name kadafi is often spelled khadafi or gadam
e.g. NUM would be transformed into
we define the component relations irl the following way
we have very briefly introduced the sentence generator moose and then inspected the role of lexical semantics therein
the handmodeling effort for the lr parser was NUM months
by means of shared variables the partial semspec is linked to the denotation
the general rule requires that only the most preferred of all lx ssible actions should be consid ered
of NUM sentences chosen NUM were evaluated as correct or quasi correct NUM gave false alarms i.e.
NUM c NUM c NUM c NUM for NUM sentences wrong
the sentences had to have no more than NUM words and to contain no formulas or words in latin alphabet
at the third stage rare and or far links are established such as coordination of independent clauses
then the syntactic dictionary and the grammar should be extended and further debugging on real texts should be carried out
in the course of parsing only the most preferred of all fragments are built see section NUM
only if they all fail the actions of the next priority h vel are considered and so on
a segment of a sentence is called syntactically connected if a well formed dependency tree can be constructed on it
if c r NUM for all r rmax corrections can not be constructed within this mode
while some are completely fixed expressions most others are stems that can be inflected expanded or transformed to some extent
it is due to the ditference of tile viewl oint be tweei
the usages of the conjunctives tara and nara which express assumptions are explained as follows
even r r this simple example the same query can be a sked many ways the child could speak can a ptera smdon
the ordering of the input events is clearly a critical fitctor in understanding the meaning of such inputs aim parsing such astring requires a
the point should be unified with here a nd a n object specified by it should be interpreted as the last referred object
case NUM there is one mouse inl ut which points a t a specific a ldw a l ed object l tera nodoh
somewhere the system itlusl creale a jl internal representation of this query that conforms to some data or knowledge base query la nguage
ing ot ject then selecting delete from the lllel l l while saying delete the reeta ugle
simply recognizing he query concerning the elephant and pteranodon is uot enough we must understand and process theni in the correct order
one could object that an intermediate step is necessary in order to decide on the kind of attribute size age etc
step s during this step we elaborate the node event NUM which yields someone drowns somewhere where the person and the place are still unspecified
NUM lexical choice as paltem matching having planned the underlying content let s see how the dictkmary may feed back on the conceptual component
in a similar vein why bother about style spelling and punctuation and so forth if syntactic structure is likely to he changtxl
why should we cam at an early stage of the process about details if we are n t sure at that point whether the global message will meet the goal defined
blends substitutions or speech repairs like tie conquered babylon the great alexander seem all to suggest that the speaker plans his message in abstract terms
this method is applicable to rather small unaligned corpora it can extract correspondences between not only simple words but also between compound words
bank as edge of river or financial institution is the same as the distinctions made by current small scale wsd algoritbm eg
we consider two trees close if we can add delete a small number of leaves to from one of the trees and or change the label of a small number of leaves in one of the trees to get the second tree
we used thresholds t NUM and t NUM allowing an error of c NUM for each le ff label change and an error of s NUM for each insertion or deletion see section NUM NUM
this trie will compress l y l ossible redundancies in the prefixes of the vertex list sequences to achieve a certain ompa tion which hell s during searching
wltolmvor wo extcn t y going a long the trio we chock if the cut off distmwo of x and the i artial y is within the botu i slu cifiod by the threshold
we urrently treat all structural or leaf label if fere es as incurring a cost that is indel endent of the tree level at whi h he difference takes i lacc
we need to make sure that any candidate NUM refix vertex list sequence that is generated as the search is being p erfbrmed does not deviate from certain initial subsequences of the query sequence by more than the allowed threshold
one of the major disadvantages of the method is that the use of english information and sense distribution of japanese case element nouns is restricted
the following is essentially a restatement of lemma NUM in terms of forward and inner probabilities
this procedure is iterated until the parameter values as well as the likelihood converge
reverse scanning leaves outer probabilities unchanged so the only operation of concern is reverse completion
this technique is efficient as it never explicitly rejects parses not consistent with the bracketing
andreas stolcke efficient probabilistic context free parsing information can be easily incorporated to restrict the allowable parses
let i be the identity matrix over the same set of nonterminals as p
it is therefore worthwhile to minimize the total number of predicted states generated by the parser
in our case we use linking to provide bottom up filtering for top down application of productions
with bottom up filtered prediction only NUM NUM states were generated resulting in NUM milliseconds per sentence
informally a transformation based parser assigns to an input sentence an initial parse structure in some uniform way
this time is charged to the active node that turned the pair under consideration into a dead one
NUM let us compare the above result with the time performance of the standard algorithm for transformation based parsing
the authors whish to thank an anonymous referee for having pointed out important connections between tts and term rewriting systems
there are cases in which a critical rule in a tts does not give rise to order dependency in rewriting
if we extend the alphabet with variable symbols we can redefine the relation through variable substitution
the overall effect of this is that each p chain is considered in a bottom up fashion in the application of r
in transformation based parsing a finite sequence of tree rewriting rules are checked for application to an input structure
in NUM our algorithm is successful i.e. it will not identify him with john because of our previous assumption section NUM NUM
the algorithm has been implemented in common lisp sun spare to deal with pronouns as a part of a deep analyser
the resolution is organised through the following processes the expected focus algorithm that selects an initial focus called the expected focus
we recall the main ideas of the focusing approach then expand on the main hypotheses which led the design of the anaphora resolution algorithm
let k be the total number of links in the bitext and let n be the total number of co occuring word token pairs k
word order can be taken into account by conditioning the hidden parameters on the relative positions of linked word tokens in their respective sentences
we show that parse results on unedited data are worse than on cleaned up data although still very competitive if compared to other models
semantic structures of lcihcn s entry
they are also suitable to derive prototypieal situation descriptions
this section presents a number of examples for a complete overview see the corelex webpage http ca
interface list of leihen s entry
the main t oint here is that tllo
calvin believes that hot bes him the
set of so ailed theta roles oi
NUM parse trees display the exact category labels and structure represented in the penn treebank parses
if this is true it raises interesting theoretical issues about the acceptability of antecedent containment configurations
computational linguistics volume NUM number NUM each candidate is initialized with a weight of NUM
also when clause rel is the deactivated factor performance declines from NUM NUM to NUM NUM
thus the effect of this constraint is best observed in conjunction with the syntactic filter
if a vp contains another vp the two vps are set at the same level
also a baseline scheme is implemented which always selects the most recent full vp
all the examples of vpe in the treebank were coded for the correct antecedent by two coders
parse tree for get to the corner of adams and clark just as fast as you can
but note that these grammars have been written on the basis of different corpora
we have described an information extraction core system for real world german text processing
the integration of tdl and finite state expressions is easily achieved through the definition of basic edges
in the next section we are describing some of the components properties in more detail
during traversal two level rules are applied for recognizing linguistically well formed decompositions of the word form in question
it collects all verb forms used in a sentence and returns the underlying dependency based structure
hence the main strategy realized is a mix of text structure recognition and restricted shallow analysis
oktober NUM NUM h yields pp from np day
an anchor can be viewed as splitting the input stream into a left and right input part
if so agr is bound to the result of the unifier and token is bound to det
l xample NUM involves a vp anteee lent ontaining a sloppy vp ellit sis l he vp ellipsis switches from help you to kiss you
ilowever the merge of two boxes is not always possible if there is a reassigmnent of an index i will not be possible to perform the merge
the reading of interest is where the pronoun it refers to jones paycheck although its antecedent his paycheck refers to smith s paycheck
the dynamic perspective provides a kamework for a silnple intuitive account of sloppy identity and related phenomena by explaining the interpretive facts in terms of changes in context
first the dynamic individual xm is added to context this object addsan individual um to a given context such that um is of un in that context
NUM this approach will require that the sequence of terminals rewritten from the first b in NUM will be duplicated by the terminal sequence rewritten from the first instance of b if any that occurs in y
one advantage that brown et al s model i has over our word to word model is that their objective function has no local maxima
null the corrector was tested on sentences chosen at random from the russian journal computer science abstracts
the experiments are described in detail in section NUM here we present only the main results
such a dictionary containing about NUM thousand words has recently been compiled at iitp by vladimir sannikov
words of other parts of speech constitute closed classes and nmst be present in the syntactic dictionary
replacing the homonyms of those fragments by their graphic representations we obtain hypothetical corrected sentences
each of the created sentences conrains exactly r words changed in comparison with the input sentence
let fragment be a dependency tree constructed on a certain segment of a sentence
i introduction correction of agreement errors in russian texts is a problem of real practical interest
the complete adjectival paradigm contains NUM forms and the paradigm of a noun NUM forms
due to incompleteness of the grammar tile corrector fails to construct syntss for certain well formed sentences
words as the smallest units of speech that can meaningfully stand by their own they are natural units for segmentation in language processing
second it follows from this definition that many of the socalled particles which show various levels of linguistic dependencies but represent invariant grammatical functions will be treated as segmentation units
chinese on the other hand defines its sociological words in terms of characters in spite of the fact that grammatical words may be made up of one or more characters
it is nevertheless possible for a linguistic word such as a compound to be composed of more than one sociological words such as the white house
we follow the above findings and define the standard segmentation unit as a close approximation of linguistic words with emphasis on functional rather than phonological or morphological independency
ii NUM segmentatign principles we propose two segmentation principles to define the two basic concepts underlining the definition independent meaning and fixed grammatical category
since the proposed segmentation standard is intended for chinese natural language processing it is very important that it reflects linguistic reality as well as computational applicability
since these cases represent only a relatively small portion of english texts sociological words are taken as the default standard for segmentation units as well as a reasonable approximation to linguistic words in english language processing
linguistic felicity is maintained by defining a segmentation unit to be equivalent to the theoretical definition of word and by providing a set of segmentation principles that are equivalent to a functional definition of a word
surface realisation starts with the following input representation
the sentence 12f based on the denominal verb prdssuriser and which is equivalent to 12f is also present in the corpus 12f pressuriser le circuit hydraulique
r3 argument incorporation NUM look in the concept lexeme map null ping structures for a correspondence p xi v i NUM lcb NUM n rcb
from both english and french versions we can not derive in a simple way equivalent expressions based on a simple verb because of the adverbial modifiers te ventilate drily the reactor
for example this last case would occur when generating sentence se NUM rl would fail because the lexicon does not contain a mapping structure relating the atomic predicate gain access and a simple verb
at present our approach is less ambitious
potential correspondences between this input representation an the deep syntactic representations required to derive sentences 1e if and le after grammatical realisation
our objective in this project is to show how french and english maintenance procedures can be generated from an abstract representation of underlying action plans expressed in a formalism inspired by ai planning models
calculation of the similarity between the word and those in the thesaurus a sslgns it to tile area flying vehicle air plane helicopter
attribute q i l unit fl state f l operation NUM force
experiment showed lhat the fol lowing heuristic is usefll if all ullkltowil word lies conjunctive relationships with a node word in a pa rtieular area
secou NUM hese case relati nships an i e used l o i h ntify classilical iolt viewpoints for thesauruses
a suitable area if the thesaurus for an unknown wom ix estimated l y integrating the human intuition i urled in the thesaurus and statistical data extracted from the corpus
d j ai mangd pred manger i has eaten
for evaluating our approach we used the following three sets of articles in our experiments NUM articles in scientific american in lapanese
when representing the features of a text by kanji characters it is important to consider which kanji characters are significant for the text representation and useful for classification
table NUM shows the results of classifying fifll text and each chapter of a book artificial intelligence and human being
some articles in thereof contain two or more themes and these articles are classified into two or more domains by editors
so it is difficult to extract appropriate domain specific kanji characters from the articles which are classified into the authors specialties
in the statistical approach the dimensions of tim training space are too big au l tim classification process usually fails
f the document classification is ljerforined oil kanji characters we may avoid the two problenls described in section NUM
because the articles in scientific american ill japanese are not cb ssified we classified them manually
therefore instead of words we used domain specific kanji characters which appear more frequently in one domain than the other
our purpose was to re implement pronounce assess its performance and study the impact of various implementational choices on this performance
these have been tested on their ability to pronounce sets of short pseudowords previously used in similar studies as well as lexical words temporarily removed from the dictionary
it is possible to discount a very few errors as essentially trivial reducing the error rate marginally to some NUM
the correct pronunciation for these strings is taken to be that given by dedina and nusbaum NUM pp
seven subjects with phonetics training were asked to read these and give a transcription for the first pronunciation which came to mind
an arc is placed from node i to node j if there is a lnatched substring starting with li and ending with l i
note that the empty string labels arcs corresponding to bigrams the two symbols of the bigram label the nodes at either end
a logical modification was therefore to ignore those texts for which the number of common words was below a certain threshold
this was constructed using a vocabulary of NUM NUM words that was derived directly from the ernail training data
moreover they can display significantly different characteristics that affect the quality of any language models built therefrom
the net effect is that the email lm produces the highest correct and also the highest accuracy
due to memory constraints it was not possible to build the bnc models with cutoffs lower than NUM NUM
the rank correlation statistic compares differences in rank ignoring absolute value which can be significant
these motivations can be grouped into two major categories concern with social standing and identity and concern with communicational efficiency NUM methods
however whether this will generate more or less accommodation than concern for social standing generates in the human human case is an open question
the author would like to thank kyung ho loken kim yoshinori ohkubo and tsuyoshi morimoto for their encouragement help and support
the level of accommodation observed in the machine interpreted setting was both lower and less extensive i.e. it did not persist throughout the conversation
events can be defined as a change of state
made explicit in tile following representations
the other did n t mince words
she did n t let things flow
the human interpreted setting constitutes both a human human interaction and a more stressful communication environment one in which communicational efficiency is a concern
a partial drs can also contain a declared discom se referent
if instead a unique nhyp is computed using the whole hierarchy we have global nhyp
the nominal part of wordnct can be viewed as a tangled hierarchy of hypo hypernymy relations among synsets
if those relations could be given on wordnet senses conceptual density could profit from them
wordnet provides sinonymy hypernymy and meronyny relations for nouns but other relations are missing
unfortunately he applies his method on a different task that of disambiguating sets of related nouns
we also compare the performance of our algorithm with that of the most frequent heuristic
one could assume that the more context lhere is the better the disambiguation results would be
in order to fi eeze the winning sense sussna s algorithm is forced to make a unique choice
dependent is to calculate tile following values of word based likelihood and to select tile interpretation corresponding to the higher likelihood value
thus selecting a model which best explains the given data is equivalent to finding the most appropriate classification of words base t on their co occurrence
the model description length quantifies the simplicity complexity of a model and the data description length quantifies the tlt to the data
this is as if we view the noutls as entities a nd the verbs as features and cluster the entities based on their feat ures
more specifically an atttomatically constructed thesaurus can be used within its coverage and outside its coverage a hand made thesaurus can be used
in this paper we call a member of a noun partition a lloull cluster and a nlenlbe r of a verb partition a verb cluster
in what follows we will describe in detail how the description length is to be calculated in our current context as well as our silnulated annealing algorithm based on mi l
null for example in the splitting of sentence NUM into two ees see below eel does not contain non prr pronouns because it is the initial ee of the whole discourse
for example in sentence NUM while the focus algorithm would propose only john as an antecedent for him in carter s method bill will also be proposed
the focusing mechanism fails in the expected focus algorithm when encountering anaphors occurring in the first sentence of a text which we call initial anaphors such as they in sentence NUM
the set of conceptual structures of the current sentence si where the anaphor occurs the set of conceptual structures of the current elementary event eei where the anaphor occurs after the si splitting
indeed the syntactico semantic filter of the triggering rules takes into account the semantic features of words mainly verbs for recognizing in the surface sentence those that are able to trigger an elementary event
one of these criteria is for example the way in which the verb meaning influences the sentence structure then the way in which the sentence structure influences the anaphoric relations between intrasentential phrases
for the second evaluation which concerns the real evaluation of the approach i.e. without going into the practical issues concerning implementation the success rate was NUM
step NUM perform a collective evaluation i.e. evaluation that involves all the anaphors of the sentence when all the anaphors of the current sentence are processed
for example the article splendid iletirement whose main theme is the speaker s resignation of the llouse of representatives ha s an introductory part about the retirement of famous sportsmen
NUM articles NUM editorial columns in asahi newspaper ten se jingo about NUM NUM articles NUM editorial articles in asahi newspaper about
the articles of tensei jingo and tile editorial articles are classified by editors into a hi null erarchy of domains which differ from the domains of the ndc
then using these domain specific kanji characters we classifted editorial columns tensei jingo editorim articles and articles in scientific american in japanese
considering these disadvantages we propose a new method of document classification on kanfi character s on which document classification is performed without a morphological analyzer and lexieel knowledge
his approach offers advantages in terms of development and maintenance but the quality of the results is not good enough in comparison with the semantic approach
of course in our approach the quality of the results may not be as good as lit the i revh us al preaches ilsilig the words
ellison s fsts transform output candidates to harmony marks even so the inversion of these fsts are useless
the derivational grammar is a transform that one can invert using fsts searching for the input using the output
in a derivational paradigm figure la the input and output forms are enclosed in the same space
there have however been no computational models of ot parsers that derive underlying forms from the surface form
NUM we now consider the align prefix constraint restricting the prefix to occur as early in the word as possible
nevertheless the system can not parse the output string back into underlying surface forms
ellison augments the product procedure so that harmony marks are concatenated by the resulting transducer
these systems were designed as generation systems deriving surface forms from an underlying lexicon
systems of this type are likely to become even more complex as more information such as moraic structure is added
for our tagalog example we add two tapes for the underlying word and prefix forms figure NUM
thus in iteration NUM the similarity of this sentence to the medicine sense was NUM NUM vs similarity of NUM NUM to the narcotic sense
t robability an l we should gel tile same results
by relating the psemspec to the sitspec via the denotation
move NUM object water l path path NUM source tank i
simple cases are stative verbs like to own or to know
author s address tu berlin proiekt kit vmll sekr
partial sernspec psemspec the contribution that the lexeme can make to a sentence semspec
one of the constraints for sentence production is that every node be covered by some lexeme
levin discusses a causative inchoative alternation that applies to a large number of verbs
cu l m nation culmination figure NUM situation types in the ontology of moose
b NUM NUM speeding up matrix inversions
sitspec a sitspec is meant to be neutral between the target languages and between particular paraphrases
investigate alternative ways to compute compatibility degrees for hand written constraints
since it must be satisfied at the action s starting time t it is represented by t t c
in this conditions relaxation only performs better than hmm for the small corpus sn and tile bigger the corpus is tile worse results relaxation obtains
convergence has been proven under certain conditions but in a complex application such as pos gagging we will lind cases where it is not necessarily achieved
advantages of the algorithm are its irighly local character only the state at previous time step is needed to compute each new weight
all this possibilities together with all the possibilities of the relaxation algorithm give a large amount of combinations and each one of them is a possible tagging algorithm
this paper describes some experiments performed applying it to pos tagging and the results obtained it also ponders the possibility of applying it to word sense disambiguation
several parametrizations for relaxation have been tested and results seem to indicate that support function NUM NUM produces clearly worse results than the others
we have t een using support fimctions that are traditionally used in relaxation but we might try to st ecialize relaxation labeling to pos tagging
since it acts as an approximation of gradient step algorithms it has similar weakness found optima are local and convergence is not always guaranteed
since the number of variabh s lind word position will vary from one senten e to another constraints are expressed in relative terms e.g.
a pattern is a pair of cfg rules and zero or more syntactic head and link constraints for nonterminal symbols
we also plan to extend the robotic vision capabilities with additional hard software to allow visual object recognition for lexical acquisition
the funtran functional translator module takes tinsel and focal output and constructs a quantified logical form suitable for evaluation in the runtime environment to issue a command or query to the target application
the intervr speech component has a vocabulary comparable in size to that of eucalyptus but a more constrained input range about NUM million utterances mainly due to a less liberal variety of np determiners
since none of our interfaces to date has involved declarative or hypothetical utterances indefinite expressions a fighter are interpreted strictly as closed world references i.e. one of the known fighters
the three subsequent system components were developed at our own facility
would resolve to a town object whereas does this cross the elbe
like eucalyptus intervr supports commands queries complex reference and anaphora and np followups
however as shown in figure NUM it does assign the conjunction finder und hilfl to the category vp np ace dat hence the analysis requires that frauen be assigned to the inconsistent category np accadat
compared with the above rule based approach a statistical approach is easy to apply to larger domains since the linguistic knowledge can be automatically extracted from the corpora concerned with the domain
in the experiments we defined the two utterances with local cohesion if they had the plausibility above a certain threshold and we chose smoothing method NUM under the condition
where turn taking means that an utterance and the next one are produced by different persons and no turn taking means that they are done by the same person
the database includes about NUM task oriented dialogues concerned with several domains such as hotel reservation flight cancellation and so on and each of the dialogues includes about NUM utterances on average
in the case of japanese it is very difficult to recognize the end in utterances by using current speech recognition techniques because the sound power of an ending tends to be small
this is because the speech act types are more powerful in finding local cohesion than the verbs or the nouns as follows rl the speech act types are independent of domain
figure NUM shows a japanese conversation between a person and a hotel staff member which is an example of a task oriented dialogue the person is making a hotel reservation
nonnegatlve weights contributing to local cohesion and cohesion speechact cohesion verb and cohesion noun are functions giving the plausibility of coherence relations between speech act types verbs and nouns respectively
rich information fl r resolving ambiguities m sentence malysis including various context dependent NUM rol lems
in this way modifier m difiee relationships extracted from a context model provide clues for disambiguating structurally ambiguous phrases
in this case information in sentence NUM where the identical t repositional t hra se
irs the le trning sa nll le
a sample set of japanese texts is classifie d
here we propose the text representation by morpheme
the first is the size of the training samples
figure NUM an example article of encyclopedia
however NUM domains are not well balanced
specific kanji characl ers NUM NUM the loariling sample
where coinponent fi is the frequency ofdoniain slw
fach chess is further divided into NUM codes
each domain is ussigned by decimal codes
in order to illustrate that let us aby checking several examples we found out that this t rot erty can i e characterized model theoretically as follows
dtml variant allows us to rule out a reading if this reading of the discourse conrains redundant intbrmation i.e. inlbrmation which already follows fl om the meaning postulates
in order to illustrate this type of inference based resolution t rocedure let us consider the german sentence NUM NUM einige arzte haben eine schwester
f 21df NUM f NUM z hd NUM null it has been our experience that katz k mixture fits the data much better than the poisson as can be seen in figure NUM unlike the poisson the k mixture has two parameters tx and NUM and can therefore account for the fact that idf and fare not completely predictable from one another
rim channel model was slightly modified and realized on top of l vm a de facto standard for communication in distributed systems
these canonical codes are for syntactic filtering checking for the closest match in the classification algorithm
to get a better look at the subtle differences between document frequency and word frequency we focused our attention on a set of NUM words that all had approximately the same word frequency in a corpus of NUM ap stories
once we have computed c we use it to make the extended lexical entry more specific
let us therefore assume that only the lexical rules NUM and NUM of figure NUM are given
criteria to determine when it is most profitable to execute calls to an interaction predicate are required
finally if a lexical entry specifies c as t bothframe l clauses apply
almost all words are more interesting in this sense than poisson but good keywords like boycott are a lot more interesting than poisson and crummy ones like somewhat are only a little more interesting than poisson
NUM a more detailed presentation can be found in minnen in preparation
on the other hand lexical rules are encoded as unary phrase structure rules
NUM cb t3100sx t3100sx cf NUM 3100sx t3100sx NUM cb t3100sx er cf t3100sx er turn on einschalten place smile this example illustrates our hypothesis that intra sentential anaphors preferably co specify context bound discourse elements
NUM errors are caused by underspecification at different levels e.g. prepositional anaphors NUM plural anaphors NUM anaphors which refer to a member of a set NUM sentence anaphors NUM and anaphors which refer to a global focus NUM are not yet included in the mechanism
given these findings complex sentences can be processed at three stages 2a 2c transitions from one stage to the next occur only when a suitable antecedent has not been found at the previous stage 2liebesgeschichte
in order to evaluate the functional approach to the resolution of intra sentential anaphora within the centering model we compared it to the other approaches mentioned in section NUM employing the test set referred to in table NUM
cur NUM of them NUM NUM have an antecedent which is a resolved anaphor while only NUM of them NUM NUM have an antecedent which is the subject of the matrix clause cf
once the ot constraints are represented as fsts combining all of the eval constraints into one transducer is a straightforward product
we have used two different types of harmony marks in the align prefix and nocoda fsts representing the ranking of the two rules as suggested by mccarthy and prince
it is easy to see why the derivational method can be run backward it just retraces derivational links in the graph
match constrains the surface string phoneme to match NUM those in the word and prefix and vice versa figure NUM
by explicitly representing the inputto output mapping using two level ot we have laid the theoretical groundwork for recovering underlying forms from surface forms
in future work we will implement the extensions to ellison s algorithm allowing us to morphologically analyze cases like the tagalog example
awe have extended ellison s work by adding a third tape that marks segments as belonging to the prefix or to the word
tin l ore r the first two steps construct monolingual similarity trees of word chnnks in sentences
NUM perform node matching between trees of both langnages by using mutual information of japanese and english word chunks
in addition our method is robust and suitable for real world applications because it only assumes part of speech taggers for both languages
noun phrases verll phrases i rel osit iolml phrase and sentrnce level
first another table of size l is prepared each field of which represents a pointer to a substring
i ach apl licatiotl dom dn has va rious kinds of collocations ranging from word level to sentence level
it seems di icutt i o extend these statistical lllethods to t i roa ler
ional line r logic i ha brings with il i he benelil thai harl parsing provides for ci g parsing namely avoiding he need o recompu e intenne dial e
null the normal form proofs of this system have a straightforward structural characterisation that their main branch the unique path fi om an assumption to the proof s end type that includes no 3it follows that tile parsing method to be developed applies only to categorial systems having only implicational connectives
note firstly dmt since omt ih d formulae are all tirst order if we are a tding an alomi f rmula we nee t nly h ok to stored iml li atiomfl formula e for possible oral i
l he prol h m f valuating in lexation require ments can be ase NUM y using at bit vector e n o ling of in h x sets
indexation requirements cam l e he ked by the process of mat hing NUM o the d tabase so dial only at prot riate entries re brought out for further examination
of course anyone familiar with lambda calculus will immediately spot the flaw in the preceding proposal namely that the substitution process that is used in conversion is careflllly stated to avoid such accidental binding of w riables by renaining bound variables wherever required
and z with variable term z so that combining the former with some formula derived from the latter i.e. whose tern included z would cause the free occurrence of z to become bound giving a result such as x iz f z
using this statistic to find texts that are similar to email in the bnc could be achieved using the following algorithm NUM create a wfl for the email corpus
in this paper we have described our view of spoken language pragmatics and we have described how pragmatic information can be translated within the hybrid analogical approach
figure NUM generating a l edarative main clause
figure NUM expansion of external np ext da
an example fbr a software system develot e d
the mechanism of rule application is illustrated below
generation of new entries usually starts with verbs
figure NUM automatic generation of new entries
this transcategoriality is supported by lrs
NUM NUM approaches to lrs and their types
all affixes are assigned semantic features
NUM NUM b completed with nl type sec
now suppose that uk and uk z often co occur within their language
NUM repeat from step NUM until the model converges to the desired degree
the sequences of u s and v s represent corresponding regions of a bitext
NUM link all token pairs u v in the bitext
this feature makes the model more suitable for applications that are not fully statistical
a nd in writing this pa per
rj is the triple of labels at the start middle and end of the arrow
similarly the hidden parameters can be conditioned on the linked parts of speech
the likelihoods in the word to word model remain unnormalized so they do not compete
first the statistical model assigns a probability to every candidate parse tree for a sentence
the model assumes that the dependencies are independent so that
a determiner is associated with a partial drs which is so to speak what remains of a drs when one takes away a predicative drs
because the dependency model equations have two factors they are affected more severely by data sparseness
a training instance is any sequence of four words wlw2w3w NUM where wl w4 h and
figure NUM shows the resulting accuracy with accuracy values from figure NUM displayed with dotted lines
null amongst all the comparisons performed in these experiments one stands out as exhibiting the greatest contrast
in both cases we sum over all possible categories for the words in the compound
NUM these triples were manually analyzed using as context the entire article in which they appeared
three training schemes have been used and the tuned analysis procedures applied to the test set
the remaining compounds were assigned either a left branching or right branching analysis
in this study a variety of window sizes are used
tal le NUM shows which condition e ach of these uttt rant e s satisfies
in between these two extremes are the two bnc lms the one with the email vocabulary performs slightly better NUM NUM than the one with the bnc vocabulary
for example NUM is a lexical rule for recognizing references to money
principar and the abductive semantic interprete r share the same message passing algorithm for abduction
the higher the weight the higher th e likelihood that the assertion is true
the discourse entities are constructed sequentially by a list of equality an d inequality assertions
the organization is either a dependent or a c commanding np of the trigger word
the development of pie system started in july NUM when muc NUM was announced
the entries in the lexicon are organized in a similar way as paper dictionaries
NUM post ceo holder john smith org xyz inc
the lexical analyzer turns a stream of tokens into a lattice of lexical items
they differ only in the contents of the messages and constraints on message propagation
the sped catkins were determiued based on these require ll ell s
level NUM each mode input is contradictory the contents generated from independent lnode inputs axe contra dictory
ha s been the developnle ul of object oriented ouq utiug
we would like to tha nk prof ja tnes ilendler for his advice during this resea rch
sequential forces the user i o use the modalil ies one after another
fron the tnenu while sa ying delete the rectangle pointing at a n exisl ing object while saying delete saying delete the circle while pointing a t a recla gh whi h covers t he specitied circle saying move it
thus instead of having a central object initiating sollm sort of message passing we view each of the indi vidual interaction techniques a s producing reports concerning the events which occur and the t imitlg of these events e.g. the mouse in the aj ow s e nario will simply report mouse click xpos NUM ypos NUM start
aiming at iridependence from the doniain knowledge of objects we adopt erie of general ontologies which is applicable to almost all manuals
in contrast in the case of assumptions that is tara and nara there are no such restrictions on the subject
the latter applies only to the verbs clear clean drain empty and can be seen as the semantic inverse of the spray load alternation because one group of verbs denotes activities of placing something somewhere and the other describes activities of removing something from somewhere but both have the same holistic effect in one of the verb configurations
the participant roles stated there are either obligatory or optional in which case they are marked with angle brackets to disconnect pss x directed action actor a actee b source c with obligatory participants the verb is only applicable if the elements denoted by these participants are present in the sitspec
the notion of valency was further developed predominantly in german linguistics with a culmination point being the valency dictionary of german verbs NUM the term inchoative is used to cover a radmr broad range of phenomena including the beginning of an event e.g. to in ame or its coming to an end
i f the sitspec semspec mapping is the production of a o e NUM the partial semspecs psemspecs associated with a subset of the lexical options such that the lexemes in this subset collectively cover the entire sitspec
levin states that the alternation applies to a class of fill verbs which are many more than the four given by jackendoff and her alternation is not exactly the one we need here since it also involves a causative form deriving this however is in our framework a separate step
in our approach which is driven by the practical needs of mlg we aim at encapsulating syntactic matters in the front end generators and here look at valency in the sitspec semspec mapping when characterizing the linking between sitspec elements and semspec participants circumstances we describe valency in terms of upper model concepts
event NUM pre state fill statel value not empty container enginel content oill activity move i object oill path pathl destibatio tankl post state fill state2 value em pty container enginel
eventl pre state fill statel value not empty c0 tai er enginel co te t oill activity move NUM object oill path path1 desti atioh tank1 post state fill state2 value empty co taiier engine1
to support this schema a dedicated component named ils intarc license rver was introduced lilt stores information about the actum structure of the applieation system
starting a heavy weight system containing all the currently existing eoml onents we get al out NUM unix l roeesses requiring NUM m l memory
in case one of the dialogue partners runs into problems he or she activates the interpretation system by pressing a button and switches back to his or her mothertongue
without too much effort we were able to introduce split channels in order to incorporate visualization tools or introdnce dif null ferent modes of communication depending on the type of data to be exchanged
they are configured in a way guaranteeing that each comlxment is able to interm t with each other component it wishes to regardless of programming hmguages hardware architectures or system software being used
additional channels were added in order to satis y some needs that frequently arise during the design md implementation of b rge a l systems with heavy use of communication
if this inclmtl model is too complex uscrs will not acquire it and il the nlodcl is too situplistic its remaining details must be provided elsewhere during dialogue
its infelicitous expression was due to the fact that we wanted to cover observed ambiguity and related phenomena in one principle but failed to find an appropriate technical term for the purpose
c the system should provide sufficient inforntalion e.g. by telling that there is no red departure bul lhal lhere is a bh e del arture at the chosen hour
having expected the topic to come tip for seine time users therefore began to inquire about discount when approaching lhe end of the reservalion dialogue
the woz corpus analysis led to the identification of NUM principles el cooperative hun3an machine dialogue section NUM based on analysis o1 NUM examples o1 user systeill interaction problems
we eventually used the following two approaches to systematically discover such problems prior to each woz iteration we matched the scenarios to be used against the current dialogue model
we waut to show that the principles include the illaxillls as a subset end thus provides a corpus based confirmation of their validity for spoken human machine dialogue
tbe system should say enough and address the taskrelevant dialogue topics in an order which is as close as possible to the order expected hy users
rather his interest lies iri the inl rences which an interlocutor is able to make when the speaker deliberately violates oue of the maxims
one such verb is falar com acerca talk to about NUM a
we call the set of basenps b and the set of dependencies d figure l d shows b and d for this example
first while adjacency between words is a good indicator of whether there is some relationship between them this indicator is made substantially stronger if basenps are reduced to a single word
all other constituents spanning the same words must have probability greater than for some constant beam size NUM constituents which fall out of this beam are discarded
each operation gives two new probability terms one for the basenp gap tag between the two constituents and the other for the dependency between the head words of the two constituents
zfor example in john likes to go to university of pennsylvania all dependencies are between adjacent words
of the NUM misinterpreted pronouns only NUM inw lved a failure to establish configuratkmally determined disjoint reference both of these inw lved condition NUM and only an additional several errors could be tmambiguously traced to a failure to correctly identify the syntactic context in which a dis course referent appeared as determined by a misfireof the salience factors sensitive to syntactic context i lead s and arc s
NUM NUM viewing the description length as the energy flmction for annealing let al le l
whose initial value is NUM and updated to be NUM NUM after NUM ni trials
the realization of such an automatic construction method would make it possible to a save the cost of constructing a thesaurus by hand b do away with subjectivity inherent in a hand made thesaurus and c make it easier to adapt a natural language processing system to a new domain
we then define a probabilistic model a joint distribution written i c v where random variable c assumes a value fl om a fizcd nouu partition px and c a va lue from a fixed verb partition NUM v
we refer to the code length for the model awe refer he interested reader to eli aml abe NUM NUM for explana tion of ra tionals behind using the as the model description h ngth and that for tile data the data description length
case frame data adjacency data fl om a corpus ii starting from a single class or each word composing its own class divide or merge word classes NUM cover tge refers to the proportion in percentage of test data for which the disambiguat ion
specifically we view the problem of automatically clustering words as that of estimating a joint distributiofl over the cartesian product of a partition of a set of nouns in general any set of words and a partition of a set of w rbs in general any set of words and propose an est imation
given a model m and data k its total description length l j NUM is colnputed as the suni of the model description length l d lt the description length of its parameters i m and data description length ld t m
NUM if one of the obtained subset is elul t y t ll ll return the iloll olllpty subset otherwise recursiw ly apply clustering on both of the two subsets
our user community consists of native english speakers who want the menus and buttons to appear in english but require support for dewing foreign language documents in their native scripts as well as entering foreign language query terms in their native scripts
the interface is targeted for the non native speaker and includes a variety of tools to help formulate foreign language queries
an archival database using the fast data finder was implemented using paracers batch search server bss product
it currently supports paracers fast data finder search engine with support for excalibur s retrievalware currently being developed
for example the name kadafi is often spelled khadafi or gadsm
null one of our design objectives was to handle multiple search engines within the same user interface tool
vp i vp NUM x vp NUM head vp NUM head vp NUM subcat first vp subcat first vp NUM s bcat rest first x vp NUM subcat rest rest
although our proposal was developed lbr left corner parsing with backtracking it obviously carries over to other parsers with top down prediction
only when a link between numerical pointers is first found is the linking relation between feature structures used to instantiate information
there is inheritance from intension to extension of the same world but the extensions of two different worlds do not communicate
but unlike shieber our restrictors are computed automatically by building the generalization of the occurrences ofleftrecursive categories in a grammar
in contrast top down predictive linking provides for a directed search since top down instantiation can propose a category
ifere we still refer to another type having with meat the satne generic class food in the lattice of types
world is the formative functor of name the variable m is the value of the world that the discourse created
compound tenses and passive according to the microsemantic point of view the auxiliaries appear as a mark of modality of the verb
the rhetorical parsing of natural language texts
when the domain t lan rl is obtained r5 is given as the initim communicative goal
an example of an integration of a user defined tagger between the sentence recognition level and the word recognition level of the th tool is given below
alep provides a facility tsls rules which allows the grammar writer to identify information which is to flow from the tti to the linguistic processes
ps both the word structure and the phrase structure conlponent are based on the same snlall set of binary schenlata closely related to hpsg
requires an umlaut yes stenr if the stem is capable of ulnlautung at all which is the case for gibst
NUM prepositional contractions zum zur am etc appositions prof dr robin cooper we will examplify the technique for quantities
the alep platform provides a th component which allows pre processing of inputs it converts a number of formats among them latex
this is why this paper does not focus on one single topic but tries to represent the ulajor achievements of the whole of the system
these ambiguities are not introduced in the tlm lexicon as the only effect would be that a great nunlber of scgmentations would go into syntactic analysis
the rules are composed of constants typed variables functions and predicates
figure NUM the rules used to calculate the shortest path
the next speech input is rescored using this language model
this allows us to determine the objects by compatibility check
underspecified feature structures explicitly represent the differences between feature structures
naturally feature structures are well suited for representing paxtial information
ithe functionality is foreseen to allow interactive error recovery
the rules are evaluated using a forward chaining inference procedure
just like in case of shieber s bottom up generator bottom up evaluation of magic compiled grammars produced with this magic variant is only guaranteed to be complete in case the original grammar obeys the semantic monotonicity constraint
strict bottom up generation is not attractive either as it is extremely inefficient one is forced to generate all possible natural language expressions licensed by the grammar and subsequently check them against the start category
paragraph breaks are omitted to conserve space
among the blackboards there is one distinguished blackboard called the discourse blackboard that stores different levels of representations of the discourse history
if the semantic representation of the text is compat ible with the underspecified feature structure the representations are unified to reduce ambiguity
since two adjacent simple sentences in a complex sentence are combined together by the conjunctive postposition that indicates the relationship between theln using the intorination of the conjunctive postposition might ilnprove tim pertormanee of the zero pronoun resolution
assuming that became selects the underspecified category noun the features associated with the coordination subsume the features associated with each coordinate as required by rule NUM so 2b has the well formed structure shown in figure NUM
ever it is usehfl to store a NUM it ve tor m oding its requireme nt s for an appropriately indexed argu menl i.e. with 0s instantiate l for tim em nts of the impli ational s own index sel to enfor
in the following sections we will describe in detail the different knowledge bases cf
this layer transforms the information from the database into a feature bundle containing the application specific features
in each of these figures the first number is a sentence number the second number is the number of supporters in group b and the last part is a rough english translation
for the other general NUM articles used with group b the estrangement values of weight set NUM are also better than those of weight set q NUM
nit is sufficient to check in fire last phrase for japanese sentences because a predicative phrase is always located at the end of a japanese senteltce
the estrangements of the articles in figures NUM and NUM are as follows from this result the weight set NUM calcu null to adjust these heuristics for the given text
NUM if the ratio of the number of selected sentences to the number of sentences in the text exceeds the specified one then terminate this process otherwise goto NUM
weight set NUM for general articles implies that sentences near the beginning are more important than ones near the end and insistence type sentences are less important and so on
as a result most contemporary systems simply strip out punctuation in input text and do not put any marks into generated texts
however in NUM there is no phrasal item for the punctuation to attach to and so its use is unsanctioned
the most appropriate method would seem to be a combination of the two integrated methods above combining their modularity flexibility and power
chererore NUM can NUM deg po tnlatdeg as general colon exl ansion rule
parsed corpora are processed to extract punctuation patterns which are then checked and generalised to a small set of general punctuation rules
NUM also spurring the move to loth diaper covers with wdcro fasteners that eliminate om need for safety pins
some work has been carried out concerning punctuation and parsing but much of it seems to have been rather ad hoc and performance motivated
the reason for this non treatment has been the lack of any coherent theory of punctuation on which a computational treatment could be based
however no correction is made to the probability estimates for pr tl t2 and pr q t3 for unseen cases thus putting the dependency model on an equal footing with the adjacency model above
a test set of syntactically ambiguous noun compounds was extracted from our NUM million word grolier s encyclopedia corpus in the following way NUM because the corpus is not tagged or parsed a somewhat conservative strategy of looking for unambiguous sequences of nouns was used
NUM count wl w2 vr tl t2 ambig wl ambig w2 wlfitl w2qt2 count wl w2 where jc ambig wl ambig w w2et2
for n NUM let countn nl n2 be the number of times a sequence nlwl wins occurs in the training corpus where i n NUM note that windowed counts are asymmetric
NUM given two thesaurus categories tl and t there is a parameter which represents the degree of acceptability of the structure nine where nl is a noun appearing in tl and n2 appears in t2
in such cases we observe that the test instance itself provides the information that the event t2 t3 can occur and we recalculate the ratio using pr t2 t3 k for all possible categories t2 t a where k is any non zero constant
in the case of the pattern training scheme the difference between NUM NUM for adjacency and NUM NUM for dependency is statistically significant at the NUM level p NUM NUM demonstrating the superiority of the dependency model at least for the compounds within grolier s encyclopedia
here f a is an estimate of the probability that any given candidate phrase will be accepted by the spotter and f r is the probability that this phrase is rejected i.e. f r l f a
we view the semantic categorization problem as a case of disambiguation where for each lexical entity considered words phrases n grams a binary decision has to be made whether or not it is an instance of the semantic type we are interested in
NUM co a gabclli chairman of gabclli rcb mds inc clmude n lcb osenl erg is nmmed president of skandi naviska enskilda bankcn be come vice hairntan of lhe
other entities may be less context dependent e.g. company nan ms if their definitions are based on internal context e.g. ends with co as opposed to external context e.g. followed by mauufactures or if they lack negative contexts
other less s tlient entities such as products equipnmilt foodstuff or generic refcrenc es of any kind e.g. a lapanese automaker could only be i lentifled if a sut iciently detailed domain model was available
also note that the quality of the eeds affects the per formalice of the final si otl er since they define what type of lcb i1 NUM NUM the system is supt osed to look for
this work is supported by nsf grant iri9416916 and by a rehabilitation engineering research center grant from the national institute on NUM
when any of the four mismatches we have described occur mal rules that encode the conflicting realizations should be included in the language model
we see our model of the user including his her placement in slalom as capturing the zpd with respect to second language acquisition
however if the level of english acquisition is taken into account via a model like slalom significantly better correction can be given
the student s text is first processed by the error identification component which is responsible for tagging all errors found in a given input
our work differs in that we attribute many errors to language transfer lt between asl and written english as is explained below
our belief is that in order to identify probable sources of errors the developer must take into account the student s generation process
a student would use the system as a tutor entering texts perhaps of several paragraphs in length that he she has written
the analysis supports the hypothesis that these people are using the natural and beneficial strategy of building on their asl knowledge when acquiring english
in other words the eventual system must possess an understanding of what is causing the student to generate sentences that contain these mistakes
thus given a context free grammar for ww r and a string of length n then in the worst case it will take an amount of time proportional to the cube of the length of the string to determine whether the string is in ww r and identify its structure
the body contains the literal magic p t and all the literals that precede qi in the rule
this way of following the intended semantic dependencies the dependency constraint is satisfied automatically for a large class of grammars
furthermore it enables a reduction of the number of edges that need to be stored through unfolding magic predicates
for expository reasons the phonology and semantics of lexical entries except for vs are abbreviated by the first letter
dotted lines are used to represent when normal facts are combined with magic facts to derive new magic facts
a magic literal is added to the right hand side of the rule which guards the application of the rule
as a result of characterizing filtering by a definite clause representation magic brings filtering inside of the logic underlying the grammar
however consider the magic rules NUM and NUM in figure NUM rule NUM is more general than rule NUM
the table NUM provides the accuracy rates of the two parsers
NUM close the display the window
prepositions restrict the microsemantic assignment of lhe objects they introduce
around four utterances over five ps NUM NUM
each cell of the network corresponds to a specific lexeme
these constraints can be relaxed by considering the primable words
then these contextual cells modulate the initial priming activities
we will now describe the propagation of the priming activities
we will now study several linguistic phenomena the parser masters easily
its activity depends on the semantic affiliations of the priming words
this is r uts na la
it is known that as a method of estimat ion
this empirical finding verifies the independence assumption widely made in practice in statistical natural language processing
able i shows the average perplexit y
pendencies found by our method seem to agree with human intuition
mi l is guaranteed to be near optinm l
the nlore appropriate of the two in eft iterations
in particular we l ropose a method of learning dependencies between case frame slots
ps l xwitfl NUM are to be compared c f hindle
it is difficult to compare any kind of performance since their tagset is very small i.e. NUM tags including a number of wordspecific tags which reduces further the number of real tags and does not account for several morphological features such as gender number for pronouns etc
in this case not only do bigrams have precedence over unigrams but the choice of the tagging sequence p nmp is also better than the sequence p jmp as it takes into account the context information
however since all of them belong to the same genotype the NUM unseen occurrences are properly tagged
in biology the genotype refers to the content of genes or the pattern of genes in the cell
with this representation we are able to prefer one n gram decision over another based on the cost
results are even more convincing when genotypes are used in context and bigrams and trigrams are applied to disambiguate
for example the word marine with the eight morphological analyses fisted in table NUM has the genotype jfs nfs nms vlspi v1sps v2spm v3spi v3sps NUM each tag corresponding to an analysis i.e. the list of potential tags for marine as shown in table NUM
for instance the word ills son singular and plural threads with the low frequent genotype nm nmp or the word avions planes we had which belong to the genotype nfp v1p
from the inflectional morpheme similarly one could estimate the probabifity of the inflectional morpheme given its stem
circus extracts information using domain specific structures called concept nodes
however annotating a corpus is time consuming and difficult
figure NUM shows the steps involved in dictionary construction
autoslog uses several rules to recognize different verb forms
NUM NUM autoslog ts automated dictionary construction without text annota
figure NUM tst3 text classification results for different dictionaries
figure NUM tst4 text classification results with different dictionaries
figure NUM autoslog ts dictionary after frequency and relevancy filtering
autoslog ts produced a dictionary of NUM NUM unique concept nodes
stage NUM statistically filtering the concept nodes NUM
we have presented a fully trained statistical natural language interface system with separate models corresponding to the classical processing steps of parsing semantic interpretation and discourse
i think part of the confusion lies in the distinction between contrastive and presentational 2the centering analysis is inconclusive in some cases because the subject and the object in the sentence are realized with the same referential form e.g. both as overt pronouns or as full nps
given the semantic representation for the sentence the discourse model of the text processe l so far and the ranked c lists of the current and previous sentences in the discourse the following algorithm determines the topic of he sentence
thus the cf list for the rei resentation give pat chris book is the ranked list pat chris book where the subject is assmned to be more salient than the objects
the algorithms can also utilize long distance scrambling in NUM rkish i.e. constructions where an element of an embedded clause has been ex null tracted and scrambled into the matrix clause in order to play a role in the is of the matrix clause
then in section NUM i present algorithms for determining the topic and the focus and show that we can generate contextually appropriate word orders in rkish using these algorithms in a simple mt implementation
first the algorithm tries to choose the most salient discourse old entity as the sentence topicf if there is no discourse old entity realized in the sentence then a situation setting adverb o the subject is chosen as the discourse new topic
for example the b sentence in the following text is translated using long distance scrambling because the talk is the cb of the utterance and therefore the best sentence topic even though it is the argument of an embedded clause
in order to simplify the mt implementation i concentrate on translating short and simple english texts into turkish using an interlingua representation where concepts in the semantic representation map onto at most one word in the english or turkish lexicons
according to the definition of translation induced by a tts a critical rule should always be applied in post order w r t the nodes of the tree to be rewritten
in order to present our algorithm we abstract away from the assignment of the initial parse to the input and introduce below the notion of transformation based tree rewriting system
under the linear implication operator o i.e.
a compilation chart method for linear categorial deduction
for an even more complex formula e.g.
note that the string position variable i appears in both resulting formulae
for example the higher order formula would compile to two indexed formulae
let us illustrate how this can be achieved with a simple example
in the first phase a decision tree is constructed in the standard fashion using entropy reduction to guide the construction process
lexical accommodation we measured lexical accommodation by exmnining the number of lexical items which were used by both interaetors in the course of a conversation
based on four distinct factors that we have identified we propose a model of spoken language production that we call the cascaded noisy channel model figure NUM
the performance of each approach was evaluated on the basis of the number of correctly assigned class codes
in other words co occurrences with verbs may not have captured the classification basis of bgh very well
we investigated the overlap of words that were assigned correct classes for our category based method and nakano s method
NUM NUM a hybrid analogical method for speech translation
however it is necessary to handle complex sentences that are prevalent in naturally occurring discourses with the centering algorithms
4in parentheses we show the direct translation of conjunct ive postpositions into english
here this interpretation that taro lid not notice jiro fits our intuition
und rstood through tim c ntt xt yoshimoto NUM NUM
which is of an obligatory case and which is not xprossed but m NUM o
the centering theory therefore has not adequately addressed the way to handle complex sentences that contain nmltiplc verbs
this constraint is used for ranking discourse entities in the order of preference as the antecedent of a zero pronoun
think they should bc i osi o d with t corpus of naturally occurring discourses
figure NUM cascaded noisy channel model of spoken
NUM has it already been offered as a proper noun but was it rejected
the number of similarity calculations can be reduced to the number of clusters categories saving on computational resources
this experiment only demonstrates a small part of goal directed summarization
the fourth sentence in our japaneseto korean jk translation snapshot figure NUM is a sample of this type which is properly dealt with by ti mt
the semantic analyser goes directly fi om gralnmatical relations to concet tua relations without any interme liate selnantic ret resentatioll
our grammatical relations include oblique complements so that prepositions in our semantic lexicon are expressed under this second paradigm fig NUM
this has the advantage of factoring knowledge at the conceptual level rather than having to distribute it at the level of words
replacement as described above maps every u lcb on the upper side unambiguously to the corresponding li on the lower side but not vice versa
l hey both have at least some knowledge of english and use english as a common hmguage
this is comprised of the basic functionality of ice itself and a set of interface functions for different programming languages
further the testbed manager collects data about the operation of the components and visnalizes this intbrmation using the gui
note that data sent by component b arrives at a unaffeeted from modification by component c
they do not prescribe the engineering approaches used to implement the individual software components themselves
it has been developed and extended for almost seven years now and is very reliable
distributed within tire applications and with regard to the degree of interactivity involved in processing
ice is used for the w rious i rototypes of the interpretation system
making the objects involved in communication explicit offers several ways to manipulate them
for each segment the system uses the version space algorithm to search for the proper statement of the context
however when trying to learn phonological rules from linguistic data the necessary training set may not be available
figure NUM shows a tree induced at the initial state of the transducer for flapping
the ilex intelligent labelling explorer project has been set up to study this domain and to reproduce some of its distinctive features in a hypertext system
to achieve this goal our system must support mixed initiative dialogue there is a degree of dialogic interaction between the tour guide and the visitor
our peint of departure has been to gather transcripts of real guided tours in collaboration with the national museums of scotland
ll esolution of an ambiguity as in NUM is possible if it is embedded in a discourse which provides disambiguating information
consider the inconsistent lexically ambiguous sentences 13a c whose sister readings are expressed in english by 13b d
decidability of the lexical disamhiguation problem results nevertheless from the fact that lexical disambiguadon does not involve a complete understanding of the discom se
that disambiguation is nevertheless possible in many of those cases can be made obvious e.g. by continuing NUM as in NUM
a complete understanding of the discourse is required but only an incomplete one that is restricted to a set of accessible consistent information pieces
let us look more closely at the pair NUM NUM and at the lexicalisation process required to produce such sentences
hence the french version has to rely on a general verb and the locative argument should be realized at surface level
a french instruction may be less specific because a conceptual argument has been left implicit while explicitly realized in the equivalent english instruction
the rule r2 maps a single concept p to a multi lexemic structure composed of an operator verb governing a predicative noun
currently mona is used for the german and italian language
from these values the problem is intuitively clear there are many easy bracket pairs that both always produce correctly and many that both almost never produce because they are too hard or the parsing systems simply never produce a certain type of bracket pair
in the human evaluation crossing error and spurious bracket pairs are to be counted as acceptable if they would fit into the correct interpretation using the style of bracketing that the parsing system aims at ignoring the style of bracketing of the treebank
in other words if a parsing system scores NUM NUM on a test in what range should we assume the estimate to be basically the same problem arises with the statistical significance of tile difference between the test score of two different parsers
if the test set is large it would be undesirable or impossible to have a human evaluate every single bracket but we can seriously reduce the workload by not considering the exact matching bracket pairs they are simply marked as accepted
but our experience is that there is no need to do that because we are only interested in the significance level of the difference between a and b and the significance level is practically the same for all values of m1 and m2 that satisfy the condition
our strategy to solve this problem is assuming there are three types of brackets namely brackets that are ahnost always reproduced those that are almost never reproduced and those that are sometimes reproduced and therefore constitute the real test between the two parsing systems
if controls were written as events they would be ant directional involving an uni directional sub ject or object arc i.e. if a control rel rs to a node of the network there need not be any information on that node baek to the control
all hough a ltmtit itm ivc mcastu o of tim criteria is ava ilal lo to sithl li fy tho lisoussion only l hcir lua litativc do iuilions will i e used
this could be interpreted NUM into fo1 as beats x y lb demonstrate how semnet can represent complex expressions consider the well known donkey sentence every farmer that owns a donkey beats it
thus from an external viewpoint the concepts should be interpreted as intensional ttowever from the agent s viewpoint they resents farmers that own donkeys so this formula is inferred by second donkey sentence formula as would be expected
in a repeated game one stage may affect the subsequent stage
strategies ors and an are defined accordingly
scommon belief about the communicated content is always obtained in both cases
figure NUM two equilibria of the meaning game in figure NUM
natural language communication is a composite game in two senses
these facts introduce several complications into the communication game
centering theory is to explain anaphora in natural language
that is both t and a could be simply regarded as c
disambiguation we can apply the same algorithm to the task of disambiguating tile sense of a word in a certain context
figure NUM an examl le of dcp s execution
for instance indicates that only the path a needs to be made explicit since its value is more specific than the corresponding input values say s and say v
on disadvantage of this simple al proach is that coreferences between syntax and semantics disappear we call the collection of these onllnon reentrancies the coref ske lcton
obviously if a parser is not restricted through additional meta constraints tile iterated al l lication of these rules could lead to all infinite computation i.e. a loop
important considerations in the design of the system were NUM increasing the imrtbrmance NUM achieving incremental and interactive behavior null NUM minimizing the ow rhead in communication between the processors
the most important aspect for the distribution of analysis tasks and for defining modes of interaction is that one of the processes must work as a filter on the input word lattices reducing the search space
parser and recognizer are increlnental and interactively running in parallel even for short utterances the lattices can contain several lmndreds of word hypotheses most of which do not combine to grammatical utterances
in order to gjuarantee correctness of tll almlysis we might unify the results of NUM oth parsers with the corresl onding coref skeletons at the end of an analysis
ill the syn parser this infermat ion might either lead to a true hart revision process or be employed as a filter to narrow the set of enfitted bottom ul hyl otheses
these results show that the modularization of the grammar and the distribution of its information lead to a considerable increase ill parsing efficiency thus ilnproving the comimtational appli ability of codescriptive grmnmars
i do not believe this to be the right approach because it blurs the distinction between related systematic polysemy and unrelated senses homonymy bank bank
the final step in the process of adapting corelex to a specific domain involves the translation of observed syntactic patterns into corresponding semantic ones and generating a semantic lexicon representing that information
constitutive x act v y relation v z act relation telic p event acterelation a act r1 a relation r2 rs
mi is defined in general as follows null
the remaining NUM are nouns that do have an indefinite number of different interpretations hut all of these are somehow related and should be inferred from a common knowledge representation
for instance the seven different senses that wordnet assigns to the lexical item book see figure i above can be reduced to the two basic senses art corn
centering algorithm to timse sentences tile process becomes as follows
by the very nature of its construction a general grammar allows a great many theoretically valid analyses of almost any non trivial sentence
in figure NUM the transition labels are triples of the sort wi pj o for the jth parse of token i with the NUM indicating the initial vote of the parse
NUM uygulama application the main intent of our system is to achieve mor null phological disambiguation by choosing for a given ambiguous token the correct parse in a given context
the preprocessor also uses a second morphological processor for dealing with unknown words which recovers any derivational and inflectional information from a word even if the root word is not known
a post mortem analysis has shown that cases that have been missed are mostly due to morphosyntactic dependencies that span a context much wider that NUM tokens that we currently employ
even at m NUM NUM there is considerable loss of precision and going up to m NUM causes a dramatic increase in precision without a significant loss in recall
on the other hand a text where each token is annotated with all possible parses NUM the recall will be NUM NUM but the precision will be low
we have applied this approach to turkish a language with complex agglutinative word forms exhibiting morphological ambiguity phenomena not usually found in languages like english and have obtained quite promising results
NUM for static vote assignment intuitively we would like to give high votes to rules that are more specific i.e. to rules that have aassuming no unknown words
we have presented an approach to constraint based morphological disambiguation which uses constraint voting as its primary mechanism for parse selection and alleviates the rule developer from worrying about rule ordering issues
the standard measure by which lms are assessed is by calculating their perplexity using a sample of test data
by ignoring root stem features during this process we essentially are considering just the top level inflectional information of the parses
simplified english and rationalised french suggest to restrict the use of operator verbs assuming that verbs that directly show the actions make maintenance instructions clearer
we will also show later that sometinms operator verbs can not be avoided when some attributes of the action to be performed should be conveyed explicitly
let eg va be the set of bilingual surface case strnctures collected from the japanese english parallel corpora each element of which has a japanese verb va
first sense distribution of english predicates and japanese case element nouns is represented using monolingual english and japanese thesaurus respectively sections NUM and NUM
we ewduated the accuracy of the method am the rate of the number of examples contained in one sense clusters as in the eg sub eohmm
next we introduce subsuraption relation of a bilingual surface case structure e and a japanese ease class frame fa e f f3 iff
when a japanese noun nji tins several senses it may appear in several leaf classes in the lapanese thesaurus
in this paper we describe how this idea can be applied to the sense classification of japanese verbal polysemy in case frame acquisition from japanese english parallel corpora
in bgh the nouns watash i and uwagi have only one sense respectively and kagi has four senses
secondly and possibly even more importantly the number of specialized rules produced by a given training corpus is approximately halved
the verbs unlock and dgfreiner have a very close meaning but tile second one is domain specific and imposes more c nstraints on its second argument the direct object
trie but this does not iraprow the execution time
NUM NUM error tolerant matching in the trie
if however ditl rences that ar0
we consider an extra or a missing leaf a s a structural change
but the entries in prior c ohumls m o still valid
l gt c l t can be proven with mathematical induction from the fact that every valid derivation sequence of gt satisfies head constraints of corresponding rules in t
in phase NUM head and link constraints are examined and unification of feature structures is performed by using the charts obtained in phase NUM candidate patterns are ordered by their weights and preferences
at an early stage of grammar acquisition addition of new patterns was primarily used to enrich the set t of patterns and many sentences were unambiguously and correctly translated
will generate the following NUM n synchronized pairs of charts for the sequence of n l nonterminal symbols aaa ab for which no effective packing of the target charts is possible
NUM for each nonterminal symbol x in t gt ineludes a set of nonterminal symbols lcb x w is either a terminal symbol in t or a special symbol e rcb
moreover defining a new stag rule is not as easy for the users as just adding an entry into a dictionary because each stag rule has to be specified as a pair of tree structures
similarly we have proposition NUM let a cfg h be a subset of source cfg skeletons in t such that a source cfg skeleton k is in h iffk has no head constraints associated with it
such a form has been chosen both for complexity reasons and to decrease the number of cases we have to deal with
though this o n NUM upper bound does not improve over previous results the average case behaves much better
rl rn is a rightmost s x derivation in g this shows that the rightmost derivation language of a cfg is also cf
however this result is not very interesting since individual parse trees can be as easily extracted directly from the parse forest
relations can be used to direct the parsing process and decrease the parsing time see section NUM
we study parsing of ligs our goal being to define a structure that verifies the lig constraints and codes all and exclusively parse trees deriving sentences
moreover it is problematic that a worst case polynomial size structure could be reached by some sharing compatible both with the syntactic and the emantic features
however we wish to go one step further since the parsing or even recognition problem for ligs can not be trivially extracted from the liged forests
a b and c so the number of productions of that form is cubic in the number of non terminals and therefore is o n6
this shows that the cutting out of any string of length l into elementary pieces of length NUM is performed in using o l productions
in NUM we find the lexical entries of NUM after their reshut ing according to manning and sag s proposal for the sake of readability the representation of subj and
and a the reflexive occurs in objective constructions respectively as an immediate constituent of vp and as an immediate constituent of s the corresponding active constructions are displayed in NUM b
moreover there is a fourth assumption which proposes that principles a and b should be validated in at least one of the two arg s features occurring in the derived lexical entry of a causative verbal lbrm
in a linear order two cases occur either x precedes y or y precedes x l heretbre x does not o command y iff y precedes x i.e.
in this paper it is argued that the accuracy of the syntax semantics interfhce is improw d by adopting u non linear obliqueness hierarchy br subcategorized arguments
in iips framework this ibatuce has been shown to be a critical point of articulation t etween highly autononmus principle based syntax and semantics cmnponents vd
in NUM b the reflexive is bound by a more oblique element in the subcat list in violation of principle a but the construction is acceptable
notice however that in the examples above we were mainly concerned with the validation of principle a consequently in those examples one was checking only whether a given x preceded a certain y
the contrast of NUM is correctly accounted t or because john is less oblique than himself in NUM b but it is more oblique in NUM a
the NUM is giv m sin e the shortest collocations are of length NUM and we want them to be of ilnportan e NUM NUM NUM
NUM we continue with the two NUM grams
table NUM n grams extracted by cost criteria con
smadja s xtract produces only the biggest possible n grams
ht section NUM collocations arc briefly discussed and the
the corpus consists of NUM NUM words of market reports
a second fa tor is tit
we extract uninterrupted and interrupted collocations
this is the most complicate ease
two explanations for this are possible
oihihhihhx ibncii oikik91k97 ibncii oicicricrm ibncli oihihhihhv
the result for the pp testing is highly revealing
the first of these was the email lm
similarly the sciences attract each other e.g.
figure NUM example of a hybrid decision structure
the problem as we ha ve already said the two al tnoaches t o senl entim negai ion differ with rt st t ct i o the scope they assign i the negation ol r rtor
roughly the interpretation of a negated sentence induces the following steps ill tile construction of a drs introduction of a location time t introduction of a condition relating t with the speech time n introduction of a con lition saying that there is no event or state of a certain l yl e which stands in the relation c or NUM o t
this analysis gives tile correct prediction for the contrast in 5a bi lcb l s smiling is interpreted as a reaction to mary s looking at bill thus following it whereas in 5b bill was already smiling when mary looked at him
for the time being the discourse structure is a list of the generated representations
irregular lexemes are standardly regular in some respect
figure NUM shows examples for a typed feature structure and an underspecified feature structure
thus an incremental strategy that fixes partial results is necessary for efficient processing and is achieved by bottom up processing in left to right order
we will show bottom up pattern application by translating the following sample english sentence into japanese thc bus goes to chinatown at ten a m
one is the combination of NUM and NUM where x at f is a noun phrase
a variable on a given level is instantiated by a string on the lingustic levels in the second column of table NUM
indices to possible patterns are obtained from several words and bigrams in the above m rkerinserted string table NUM
if a passive arc is generated in this operation repeat the procedure until a new arc can no longer be created
the functional word the which is relevant to the pattern a x creates an active arc
the most appropriate st ructure is selected by computing tile total sum of all possible combinations of partial semantic distance values
the structure with the least total distmme is judged most consistent with empirical knowledge and is chosen as the most plausible structure
NUM note that complex expressions require path embedding
the lexicalisation of arguments involves other mechanisms which concern in particular the construction of referring expressions NUM
constants stand for atomic feature structures whose type is given by the constant name
in r2 the attribute will be reatised as an adjective which linked to the predicative noun n with an attributive relation
on the contrary by using earley style parsing with a set of carefully designed and estimated fault tolerant top level productions it should be possible to use probabilities to better advantage in robust parsing
the number designates the actant of the predicative noun which is promoted as first actant syntactic subject of the operator verb
when the method extracts a longest n gram as a chunk strings subsumed by the chunk are derived only if the shorter string of tell appears independently to the longest chunk
these quantities are then summed over all nonterminals z and the result is once multiplied by the rule probability p y u to give the forward probability for the predicted state
earley s control structure lets the algorithm run with best known complexity on a number of grammar subclasses and no worse than standard bottom up probabilistic chart parsers on general scfgs and fully parameterized cnf grammars
a substring pointed to by the i NUM th entry of the table constitutes a string existing from the ith character to the end of the text string
in ollll t qt enl lish ha s ouly many useless collocations can be removed by imposing this constraint on extracted strings
the research described in the previous section deals with character based n grams which generate excessive numbers of expressions and requires large memory for the pointer table
this is ha sed on the intuitive ide tim if a set of words onstitutes a collocation its subset will mso be correla ted
for examphi no NUM nieans tokyo ohl fulure m rkel ended trading r r the lay but was never written as such
the intended effect of the ppi i lcb is to promote the direct object of a transitive verb in the l l for the actiw form to the subject of the passive form
words not covered by the parse trees rootes at the top level slots are ignored
der hund wurde vergesscn zu fpsttcrn it was forgotten to feed the dog however we are not aware of any german speakers that would allow passives with raising verbs such as k6nnen
y contrast if the feb lion between the mother category ttnd each daughter category is that of unifialfility then the resulting gra mm tr vastly overge erates
NUM ungrammatical sentences such as NUM can be successfiflly ruled out if the pplr is applied to an i e only if the input specification of the lr subsumes the le hypothesis b
in the case at hand the list of raised arguments in the le for kdnnen in fig NUM is totally unspecified it can be any list of non verbal synsem objects inchiding the empty list
the del endency analysis is represented with the function dep f rs where f is a dfs and rs is a restriction schema used in generation of las definition NUM dep for a feature structure and the
the major difference of step NUM and the normal application of a rule schema is that non head dtr values are not specified in step NUM in spite of this underspecification certain parts of the non head dtr are instantiated because they are token identicm with certain values of the head d r domain
though theoretically very attractive codescription has its price i the grammar is difficult to modularize due to the fact that the levels constrain each other mutually and ii there is a computational overhead when parsers use the complete descriptions
as was the case for the previous two strategies this is only necessary if a path is introduced in the resulting structure whose value is more specific than the value s in the input structure s
although senumtics construction is driven by the speech pm ser the use of titfelent subgrammars suggest that the sl e cch i mser should also be guided NUM y the sl m parsel
thus even if an utterance is accepted by g with analysis fs encoded as a feature structure it might be tile case that the unifi ation of the corresponding resuits for g v
grammatical analysis is performed by tightly coupled parsers running in tandem each using only designated parts of the grammatical description in the paper we describe the partitioning of grammatical information for the parsers and present results about the performance
completion histories are described by the following ebnf lcb r rule id edge id start end lcb e edge id rcb i l lex id edge id st art end rcb rule id lex id edge id start and end are integers
this section presents experimental results of our compilation method indicating that the simple syn sem separation does not match the distinction between true and spurious constraints mostly due to semantic selectional constraints see fig NUM
in a topic comment structure the topic is signed first and then the comment is signed grammatical signals marking the topic and comment
on the other rcb ran l too ma ny relationships prevent the ext raction of useful view points
verb second posid m is handhxl by a mechanism resembling cite notion of head movemt nt of b cheery
a main obstacle for the successfifl application of n i p is the necessary effort in terms of deve opinent and adaptation time
l here re in tin however i he inosi cet lcb l al lll s princ ipies
the require l syntactic features f a particular word form arc dct a mined l y the sentence level syntaeti
the correspondence between syntactic arguments anti semantic roles is established by placing the constituent under a feature corresponding to its semantic role
this view may be essential to irony but these theories ark still incomplete as a comprehensive framework h r irony for at least three reasons
principle althougtl 2t also becomes insincere when l he lnother no hmger intends her son to clean ut his room
the properties of irony allusion pragmatic insineeril y and emotional attitude arc formalized mmquivo ally enough to build a coinputational model of irony
note that this paper focuses only on verbal irony and thus situational irony i i.e. situations are ironic is beyond the scope of our theory
exmnple NUM a mother asked her son to lea it up his messy room but he did a slol py half hearted job
our representational scheme includes discrete items of intbrmation called infons situations capable of making infons true i.e. supt orting infons and actions
in order to formalize ironic utterances and ironic enviromnent ill a coint utational fashion we use situation theory barwise NUM NUM and situation calculus
they laimed that all ironic utterances allude to a failed e xpe ctation and violate one of the felicity conditions for well ibrmed speech acts
re quests often i ecolne il sincere when they are over polite like 2t since they violate the t olitene ss
n gram tatters like church NUM lelinek
NUM NUM model a bigram lexieal affinities
NUM NUM model b seleetional i references
crossing links and cycles arc not allowed
we view this problem as that of estimating a joint distribution over the artesian product of a partition of a set of nouns and a partition of a set of verbs and propose a learning a lgorithm based on the mininmm description length mdl principle for such estimation
thus st mai is represented as a set of those classes and is referred to as a semantic label
the proposed measure addresses the limitations of lexical association by facilitating sta tistical discovery of facts involving word classes rather than individual words
note that the transition function below is computed on a number of states that is independent of the degree of the input tree
index NUM is then retrieved from h and the only node in rule l i.e. mr6 is considered
since in practice only a small percentage of rules are applied to any particular structure the naive parsing algorithm is rather inefficient
the dialogue model provides general information about the structure of an information retrieval dialogue hence we consider it a representation of genre
first we show the result of classifying NUM examples represented as bilingual surface ease structures of the japanese polysemous verb kau
be the maximum number of japanese cases in a bilingual surface ease structure the depths of the japanese and english thesauri respectively
restricted to the generalization of the instances of that left recursive category
unfortunately in the data set made available in the public domain there is no indication of which sentences are used as test sentences
we woukl also like to thank xabier arregi jose mari arriola xabier artola arantza dfaz de llarraza kepa sarasola and aitor soroa fiom the computer science faculty of ehu and franeesc ribas ltoracio rodrfguez and alicia ageno from the
given a window size the program moves the window one noun at a time from the beginning of the document towards its end disambiguating in each step the noun in the middle of the window and considering the other nouns in the window as contexl
we selected four texts from semcor at random br a01 where a stands for gender press reportage br b20 b for press editorial br j09 j means learned science and br r05 r for humour
much of recent work in lexical ambiguity resolution offers the prospect that a disambiguation system might be able to receive as input unrestricted text and tag each word with the most likely sense with fairly reasonable accuracy and efficiency
discarding the factors that do not affect performance significantly NUM and obtain the results in table NUM a more thorougla comparison with these methods could he desirable hut not possible in this paper l or the sake of conciseness
we would like to have included in this paper a study on whether there is or not a correlation among correct and erroneous sense assignations and the degree of conceptual density that is the actual figure held by fommla i
as each text is structured a list of sentences lacking any indication of headings sections paragraph endings text changes etc the program gathers the context without knowing whether the nouns actually occur in coherent pieces of text
the effect of such an addition would be to increase the size of the training corpus from NUM million words to NUM million which constitutes an increase of NUM
so despite the absence of apparently suitable candidates in the top NUM the overall accuracy of this technique measured by the mean rank of the NUM ci texts is higher
we continue with the second sentence
exainlfles NUM and NUM have already been discussed
theory involves ml ad litiona l
consider the following discourse tile discourse aj farmer walks
the l ranslation for and is the sequencing operator
NUM head constraints the nonterminal symbol v in the source rule must have the verb miss as a syntactic head
NUM with all possible subsequences of length NUM classes sec
glose is composed of two mt models NUM one for each of the two languages considered in our domain
we have assumed so far that actions to be verbalised can be represented by simple predicate argument structures
for example the english sentence unlock the door is acceptable but not the french one ddfreiner la porte
the lexicallsation process the sentence generator should be able to generate multilingual pairs of instructions similar to the excerpts NUM NUM and NUM by selecting an operator verb construction for one element of the pair and a simple verb construction for the other element
for example a compositional english translation of the sentence john a posd une question d mary would lead to the incorrect sentence john put a question to mary whereas the correct or the more closely related translation would be john asked mary a question
for example the correspondence f5 NUM rernplissage used by the rule r2 when generating the sentence 1f can also be used to construct the nominalisation le remplissage de l accumulateur in the declarative sentence 6f le remplissage de l aeeumulateur dolt provoquer l allumage du voyant sur le tableau hydluulique
to make the appropriate translation an mt system should be able to identify in the initial sentence the semi idiomatic expression poser une question and consequently build a sentence based on the equivalent english expression ask a question
hence a conceptual lexeme mapping structure indicates not only which lexeme s can be used to express a concept but also how the roles of the concept should be realized in terms of deep syntactic relations
in a mtt like lexicon predicative nouns are linked to their operator verbs i y means of the lexical functions operx opera for example operl remplissage procdder
the output is a lisp like expression corresponding to the lcs representation
this is achieved by inserting a marker appropriately
in the lisp representation corresponds to the angle bracketed constants ill table NUM e.g.
table NUM shows three broad semantic categories and example verbs along with their associated lcs representations
the thematic code numbers NUM and NUM respectively are marked
we have described techniques for automatic construction of dictionaries for use in large scale flt
lcs templates for NUM additional classes that are not included in levin s system
er for an instrument and ed for resulting states
the dictionaries are based on a languageindependent representation called lexical conceptual structure lcs
correctly the author of the lesson must provide the desired response in advance
NUM c c propositional the rule p allows us to replace any formula in t with a logically weaker one
in fact we are agnostic as to whether more complex feature systems for lcg are linguistically justified in any event dorre et
if adjuncts in general are treated as arguments of the head then the problem of passing features through adjunction disappears
NUM a kim v became hv wealthy np a republican b
for example since kim is assigned to the category np sga3 then by rule p it will belong to np as well
we identify a number of situations where the two accounts make different predictions and find that generally the lcg account is superior
this is possible as shown in figure NUM thus the feature structure subsumption account incorrectly predicts the well formedness of NUM
inputs of ea ch mode shouhl be interpreted independently
mm i cg has two ma jor extensions t
expressiou should ha re the following functiona lities
thus complex multi modm expressions can be defined declara tively
experimenta l nmlti moda l interfa ce system clevelopments
the multi modal met hod has been inductively defined through severa l
the following is tile trace of the lesigtt process
time col suming steps were step NUM and step NUM
therefore each clause belongs to one of the followings subject user and predicate verb in volitional use u v hereafter subject user and predicate others u o subject machine and predicate verb in volitional use m v subject machine and predicate others m o of each conjunctive
as for tile types of verbs each clause is classified into two classes according to volitionality of verb
if a verb of the matrix chmse has a non volitional use thai is it it is possible for the action of the clause to be done unconsciously the constraint is not applied because the w rb in non volitional use does not express any volition invitations requests and im junctions
one is the type of verbs which can be used tbr not only volitional actions but also non volitional actiorts
first of all in about NUM to sentences all of tile matrix clauses have no request form
generally the term subject d notes a nominative from the grammatical point of view
it depends on the volitionality of the verb whether a sentence shows a speaker s attitude or not
in order to confirm our estimation about reba tara and nara let us examine real examples
the connective particle tara or nara the matrix clause e presses only user s volitional action
as was the case with generation in english there is a high degree of overlap between all other expressions of the two parts of the relation
the expected probability of a type t with sample frequency fit r is estimated by r n where n is the total number of observed types
for time cost reasons no experiments were performed with subtrees larger than depth NUM the following table gives the results of these experiments for subtree depth NUM
after assigning the adjusted probabilities to the subtrees in the resulting parse forest the most probable parse can be estimated in the same way as in dop1 by monte carlo
a reason for these shortcomings may be the statistical inadequacy of dop2 it does not allow for the computation of the probability of a parse containing one or more unknown words
the probability of a parse tree is equal to the probability that any of its derivations occurs which is the sum of the probabilities of all derivations of that parse tree
dop1 estimates the probability of substituting a subtree ti on a specific node as the probability of selecting ti among all subtrees in the corpus that could be substituted on that node
knowing that we have to realize a question we have three mood options available declarative yes noquestion and wh question
even though current speech synthesizers can support sophisticated variation of intonation no existing text to speech or concept to speech system for german is available that provides the semantic or pragmatic guidance necessary for selecting intonations appropriately
NUM replace the idea of syntax dependent prosody which is implicit to all the approaches discussed so far with the notion of the linguistic function of prosodic features including intonation
in many contexts it is more natural to use just a phrase frankfurt am main oder an der oder similarly it is unnatural to generate the evaluate moves as complete clauses
in the speak project we have chosen to employ a modified version of the conversational roles model cor as our dialogue model see NUM
in the work presented here we assume that a component exists that can choose one of the dialogue acts from these subsets see e.g. NUM NUM
request in withdraw a request in the context of any of the unexpected dialogue moves e.g. withdrawrequest mostly serves as confirmation question similar to the responding requests in inform that we discussed above
it has been modified within the speak framework in order to include naturally occuring data that the original model failed to account for but the overall speech act framework remains the same
to this number the number of distinct unlexicalized np subtrees must be added NUM yielding NUM NUM x NUM NUM types for the total number of distinct np subtrees
the partial structure of this type of requests is as follows offer in an information retrieval system the system often offers the user a list of alternatives from which she has to choose one
we suggest that uncovering the decisions necessary for producing pragmatically appropriate sets of parallel instructions is a task best performed as an empirical study along the lines suggested here
figure NUM connecting up facts resulting from not so naive generation of the sentence john buys mary a
recall that drt discourse referents do not serve only to account for this aspectual dimension but do play a fundamental discursive role
we have seen that the representation of time in drt makes us of two discourse referents at least
in constrast discourse ret rents are only present in the form of dr variables they will be introduced by the partial drss
this discourse referent is meant to serve as an argument of the predicative drs which will be assigned to one pdrs wxriable during the conversion
this work shows the convergence of diil erent approaches fi m the syntax semantic interface point of view
correct than the bnc email lm even though both share the same vocabulary
accuracy is more critical than correct in that it directly penalises insertions
these constraints are expressed by stacks of symbols associated with non terminals
stuart lane russel l evon nilson l ahl
the following lexicon and two level grammar demonstrate how the above measures can be analyzed under two level theory
this ensures that each rule is applied only to the NUM roper ineasure
for example consider the case where a concrete domain plan r3 is obtained during the production of utterance ul
if the rules were to be compiled into automata a genuine symbol e.g.
a grammar for the derivation of the cited data appears below
there are two types of pc positive ppc and negative npc
popel can generate dis null courses using eontextum information tlowever it d les not allow for the line structure f discourse prevailing in st oken dimogues
in this as the preceding senten e joh n likes apples has the structure a likes b whereas sentence NUM has the structure x also likes b where b and the predi ate fib s are identical
the main location for punctuation marks is likely to be with phrasal level items whether the marks occur before a particular phrasal item or after it
the effect of the above statement is to say that mor form globally inherits from the path given by the atom mor followed by the global value of syn form
spell abc sabc e svow svow
it might seem at first glance that such a language would be quite inappropriate to a domain such as the lexicon where ambiguities are common
another difference lies in the fact that datr subpaths and superpaths can have values of their own dag2 v agr sing v agr per NUM
wherever this phenomenon occurs there is the potential for conflicting inheritance i.e. when the information inherited from one node is inconsistent with that inherited from another
in the case of global evaluable paths once the subexpressions have been evaluated the expression containing the resultant path is also evaluated from the same global state
second we define a similarity measure on the feature space which allows us to pool the statistics of similar features
table NUM parsing results reported by jelinek et
however the issue of applying such constraints is specific to the two treeb nkr being used there may well be cases in which such constr iuts are not hard to develop
this proved to be impractical because the constr nts were not hard i.e. the exact circ mstances in which they should be applied were di cult to determine
NUM in a second consistency experiment we located all sentences occurring twice or more in the treeb nk if there were more than two duplicates we selected just two at random
as an additioaal means of improving the accuracy of our parser we have been working towards effecting a dramatic increase in the size of our trai ing treebank via treebank conversion techniques
every question b s access to the current parse state which cont i everything known or predicted about the parse tree up to the time the question is asked
since the topology of the parallel tree may be very different from that of the atr parse tree it is not obvious what the analog of a node in the atr tree is
NUM how prediction is carried out NUM NUM
further the full range of attachment sites is available within the gr mm r for sentential and phrasal modffers so that differences in meaning can be accurately reflected in parses
the verbmobil system consists of a large number of components each of them designed to cope with specific aspects of the interpretation process
r the visualisation manager vim collects all the data transferred between any of the components using ic e channels
in this configuration we are using NUM i ase channels and NUM additional channels NUM ici channels in total
these nl p omponents are embedded in the so called testbed that serves as an application damework
the testbed provides the fa cility to choose in an oflline process the components that are desired to i e executed
furthermore split channels allow for the easy configuration of a system with respect to interchangeable parts of the system and attached visualization
ne of the lists displays the subcategorized elements according to an order relevant to their linear surl tce concatenation
the other list in lurn orders the subcategorized elements uccm ding to a hierarchy relewmt u se up the binding relations between them
taroo ngm ziro dat purposefully self acc criticize caus past tarooi purposefully made zirooj criticize himselfi j also pronouns exhibit a special behavior in the context of causatives
examples of such languages are malayalam and hindi iyom india lango ti om uganda bahasa fi om indonesia japanese korean and russian vd
where x is less oblique than y iff x precedes y in an arg s lisl this definition was shown to be adequate for the data considered so thr
maria talked about pedro to himselt this is another puzzle for the current binding theory which receives a neat solution with a branching hierarchy for the arg s value
i ut after tim reordering of subcategorized elements by the passive rule john can now bind himself as shown in NUM b
figure NUM shows the realisations the task element goal with respect to the modal system which brings into sharp relief the absence of modality from procedure
it means tltat it describe s
for example the english french translation pattern NUM
michael mcdonald as usual helped me write the final version
NUM i also went to the beach NUM weeks earlier
if the phrase leave it behind is not correctly translated
NUM he kept calm in the face of great danger
to v the set of all possible subsets of variables in v
in all corpora results improve when adding hand written constraints except in wsj
we have applied relaxation labeling algorithm to the task of pos tagging
probably adding more linguistic constraints would yield more significant improvements
table NUM best results using a specific support fun
table NUM best results using back off technique
a most likely algorithm got NUM over nouns apperaring in wn
the functional information structure has impact not only on the resolution of inter sentential anaphora but also on the resolution of intra sentential anaphora
i would also like to thank jon alcantara cambridge who kindly took the role of the native speaker via internet
j for all substrings b revise t b revise c b
for orgmdzation tagging the recall and precision results obtained after the tirst mid the follrth t rcb ootstrat t ing eyt le are given in figm e NUM
these preparatory steps are desirable since they reduce the amount of noise through which the lemrning process needs to plow but they mre not strictly st eaking ne essary
NUM what do you want to find seed selection if we want to identify some things in a stream of text we first need to learn how to distinguish them from other items
evidence items for all candidate phrases in the training corpus for those selected by tile initial used supplied seed as well as for those added by a training iteration are divided into two groups
in addition negative examples can be given if known to eliminate certain obvious exceptions e.g. not to the right of made foal not toothbrushes
examph s of extracted products include the mercury grand marquis and ford crown victoria cars tevrolet prizm pump shoe ms doe
NUM is used to combine evidences
the current program can not deal with conjunction
instead semantic tagging should be a first step in the interpretation process by assigning each lexical item a representation of all of its systematically related senses
the classification process is evaluated in terms of precision and recall figures but not directly on the classified unknown nouns because their precision is hard to measure
correct classification rather seems to depend on the homogeneity of the corpus if it is written in one style with one theme and so on
precision is much harder to measure but depends both on the accuracy of the output of the part of speech tagger and on the accuracy of class sensitive heuristics
for this purpose the pattern matcher keeps two separate arrays one that collects knowledge only on cortelex nouns and the other collecting knowledge on all nouns
in this section i describe the structure and content of a lexicon corelex that builds on the assumptions about lexical semantics and discourse outlined above
be adv verb verb noun similarly the headnoun verb patterns approach a true sub j verb analysis
this number seems to correlate with the size of the corpus in larger corpora more nouns are being classified but not necessarily more correctly
once integrated the interactions between the components have to be specified
corpus as a raw corpus and calculated recall and precision or each threshold value see table NUM
a programmable multi blackboard architecture for dialogue processing systems
moreover links exist between the objects to access the different levels
moreover a certain number of agents is linked to the system
this is done in the following manner
disambiguate as above add path src isundefined lcb obj path rcb
thus it is important to extract fixed collocations with high precision
monotonic paradigmatic schemata in italian verb inflection
these sentences cover basic expressions in an inquiry dialogue
instead of the gender being wholly determined by the sex of the referent the gender is determined partly by sex and partly by the phonology
local inheritance derives new statements associated with a node path pair but at most one of these defines a value or global inheritance descriptor since local inheritance ceases at that point
to understand why this is note first that the default extension process preserves functionality since it only adds definitional statements about new node path pairs not already present in the original description
moreover the use of this purely morphological feature leads them to introduce a set of lexical rules in order to map the relevant information across from the different syntactic features
in fact a global descriptor is implicitly present at every node path pair that could ever occur as the global context for evaluation of the descriptor at its original explicitly defined location
the complete list of reserved symbols is as follows y we have already seen the use of the first seven of these
sentences are built up out of a small set of basic expression types which themselves are built up out of sequences of lexical tokens which we take to be primitive
although this change does not itself make the description more concise it allows us to introduce other ways of describing values in definitional statements in addition to simply specifying them
the u s currency was quoted at NUM NUM NUM NUM
the data set was too small
over the course of two summer projects we developed a general purpose natural language system which advances the state of the art in several areas
note that this strategy performs remarkably well at first sight
the approach which prefers intra sentential antecedents causes NUM additional errors
awe do not consider dialogues with elliptical utterances NUM
the approach which prefers inter sentential anaphora causes NUM additional errors
table NUM centering data for sentences NUM and NUM
but these grammar theories only give filters for excluding some elements from consideration
possible strategies for treating sentence level anaphora within the centering framework are NUM
syntactic constraints like control phenomena override the preferences given by the context
hence our approach seems to be more generally applicable
in NUM cases any strategy will choose the false antecedent
two aspects of the system will be featured prominently a diagnostic tool for evaluating system output using sra s discourse tagging tool dtt and the mop pattern matching language
this removes strings arising from a partial match of different words
by thresholding the frequency only usetiff word chunks are extracted
in particular no verb phrase or sentence level collocations are not covered
most useful lapa icse lcxiblt ollocai iolls
i mae NUM exemplifies the flexible colloca tions
finally we briefly describe the coverage of the proposed method
la ol ter table figure NUM nagao s approach
only heads of each word are recorded in the pointer table
next we iteratively combin the chunks to extract flexible collocations
of are japanese english collocations whose elements constitute uninterrupted word sequences
this notation to some extent hides the fact that p is a conditional probability of production x being chosen given that x is up for expansion
however as we will see in the next section the choice of subsumption or unification makes a crucial dildrence when es are themselves highly schematic and underspecitled
the allowable sequences of lexi al rules are compiled into finite state automata which are in turn encoded as de inite clause attachments to base lexical entries
im vc i bc able ix c md inc with dill crcnl types of vcrl s g
the execution of the definite clauses of the kind shown schematically in fig NUM which encode the possible relations between base mid derived lexical entries pertains to the second task
c in other words the comics vmuc of the governed vcrh is merged with the 7omi s list of t gnncn itsclr
analogous to the applicability of tile pplr the celt lcb g is applicable to les of transitive verbs such as kaufcn shown in fig NUM under both unification and subsumption
l t ti coimtrncl ions in lift rent ulgua gcs including citric climbing in t di m mona chest
its disadvantage is that sense disambiguation is not carried out relative to any well defined set of senses but rather an ad hoc set
and my stake in the last race was a pound is stake being used in the same sense or not
below is an example of the senses assigned by the system for the sentence a rapid rise in prices soon eventuated unemployment
for example in a programming domain where one might refer to the lines of code on the screen a useful operator is llne which finds the set of all lines on the screen within the current region of focus
an important characteristic of such operators is that their syntactic and semantic portions are specified by a general purpose language c so that they can manipulate any media or semantic objects that the designer may address
in the generation mode the target meaning is known it is a particular character NUM in the example and a sequence of operators is to be found that can achieve the target meaning
as we struggled through integrating each of these new modules and discovering their dependencies on other parts of the existing system we found ourselves wishing for a standardized framework a communication and architectural infrastructure for voice dialogue systems
this grammar of course has the ability to generate a variety of outputs this character with pointer the tenth character the fifth character in the second line this character in the second line with pointer etc
with pointer figure NUM the operator grammar generating syntax to select an item on the screen
this research is supported by the office of naval research grant n00014 NUM NUM NUM the national science foundation grant iri NUM NUM and a grant from the research triangle institute which is funded in part by the army research office
other operators find other sets associated with nouns find subsets of sets as with the adjective capitalized select out individuals as with an ordinal specify relationships as with containment and call for changes on the screen as with delete
it sets off from the idea that rather than trying to write rules by pointing out the conditions necessary for the acceptance of a reading in an ambiguous case it allows the writing of such rules that discard a certain reading as illegitimate
n r number of readings n t number of word form tokens percent of the total cum lower figures NUM for word form types and NUM for word form tokens
problems of disambiguation the cgp of swahili was tested with two text corpora which had not been used as test material in writing rules e kezilahabi s novel mzingile NUM NUM word form tokens and a collection of newspaper texts from the weekly paper mzalendo NUM NUM NUM word form tokens
table NUM was constructed exactly in the same manner as table NUM only the source text being different
the output of swatwol contains such information as part of speech features features for adjectives verbs adverbs nouns numerals and pronouns as well as information on noun class marking also zero marking wherever it occurs etc
constraints contains constraint rules with tile following schema wordform operation target context condition s wordform can be any surface word form for which a rule will be written
katika select prepos i n or inf or pron select the reading prepos of katika if there is a noun or infinitive of a verb or pronoun in the following cohort
fhrthermore unrestricted features value t do not constrain the result
furthermore the representations in the blackboards allow to represent partial information and to leave disjunctions unresolved
step NUM would determine that a prepositional phrase exists after both the antecedent s head verb and the vpe and therefore would delete in his novels and tales from the antecedent resulting in the correct antecedent produce humorous effects
in addition to solving the NUM cases of subdeletion the algorithm inlxoduced NUM errors each of these new errors involved a noun phrase or prepositional phrase in the vpe that did not require the deletion of a counterpart in the antecedent
it can be argued that these errors are not the fault of the vpeal algorithm that if text is parsed as not being a part of the verb phrase then it should still not be included when the verb phrase is chosen as the antecedent
for example refer to the example from error case b NUM step NUM would locate the noun phrase humorous effects and the prepositional phrase in his novels and tales as sister nodes to the antecedent head verb produce
in our applications we primarily encode the domain knowledge in the type hierarchy
oftentimes requests formulated in natural language encode only partial information or are ambiguous
variables can be substituted by representations with an appropriate type stored in the blackboards
they ensure that a set of tgl rules possesses a context free backbone
NUM conflict resolution select an element from the conflict set
all common combinations in german are covered
reusable definitions independent from the specific non reusable parts of the grammar
figure NUM shows a sample tgl rule
although fixed collocation seems trivial more than half of all useful collocations belong to this class
note here that the coincidence between japan and china and japan and costa rica is l0 as mentioned above
in our work the distortion processes are modeled using a number of distortion operators that operate on the shallow syntactic tree of the utterance
putes statistics to identify which signatures occur much more frequently in relevant texts than irrelevant texts i.e. have a high relevancy rate
the intended propositional content of the above utterancc s can be paraphrased as follows NUM souiu no wo kotowaru no ga muzukashii
in contrast to speech performance errors natural speech properties are produced intentionally by the speaker and usually carry specific pragmatic communicative functions
b there are no sisters at all but some physicians have one to whom they are not married
by converting the whole discourse into scf we made all consistent information pieces provided by the discourse accessible for lexical disambiguation
we abstract away fl oln the others r r the sake of simplicity
NUM some physicians have a sister to whom they are mm ried
each directed link in fignre NUM represents a dependency relationship with the direction going from the head to the modifier
the reasotl we choose to use the stnrt is that the definition naturally incorporate the length into consi lcration
we justify the definition by showing how it is ahle to account for several seemingly unrelated phenomena in natural languages
in other words extrapositions are only allowed if the structural complexity of tile sentence is reduced as a result
when a phrase is extraposed the set of dependency relationships remains the same
for example the structural complexity of the dependency structure in figure NUM is NUM
and invest igate the application of structtn al complcxil y in handling extraposition in parsing and generalion
dicts that la ix much more difficult to process than lb
i feaid a dcscril tion of llockncy s lal csi l ic ur
the lexical feature values are a true hybrid mechanism where symbolic knowledge is included when the neural network signals so
since there are many features each chunk may get no one or several pairs of features and atomic values
with the chunk n label principle the feature structure has a maximum nesting depth
furthermore features may be marked as up features e.g. incl excl in figure NUM and NUM
NUM a feature value is either a an atomic value e.g.
NUM convert this into a feature structure using the algorithm of figure NUM
in learn mode manually modeled chunk parses are split into several separate training sets one per neural network
in run mode the input sentence is processed through all networks giving a chunk parse which is passed
however these limitations are only theoretical because very deep nesting is hardly needed in practice for spoken language
tiowever i clieve that there is a distinct notion of topic and fo as in turkish
clearly the system needs to alter its descriptions in the light of the objects the visitor has already heard about and to have some idea about what the visitor might find interesting
in beam search incomplete parses of an utterance are pruned or discarded when on some criterion they are significantly less plausible than other competing parses
the most interesting were those that considered idealizations of linguistic phenomena in terms of the string duplicating language ww
the basic question attacked in this paper is the following one can such a grammar be concretely useful if we want to process input from a specific domain
a number of hypertext systems incorporating nlg technology have already been developed
if we want efficient parsing we want to be able to focus our search on only a small portion of the space of theoretically valid grammatical analyses
this table makes the simplification of counting only NUM NUM matchings and is merely representative
they are nonetheless proving very useful in applications and are substantially more feasible than previous models
hong kong s economic foreground with china particular guangdong province s economic foreground vitally interrelated
our economic future is inextricably bound up with china and with guangdong province in particular
the effect of the btg restriction is just to constrain the shapes of the word order distortions
it is not our purpose here to argue that accuracy can be increased with our model
this significantly increases time complexity compared to our btg model
this in turn often depends on what the target language is
the underlying model for the algorithm is a combination of the stochastic btg and bigram models
many thanks mso to kathleen mckeown and her group for discussion support and assistance
it consisted of NUM newspaper articles from the german die zeit
the last section is devoted to the efficiency and performance of the system
completeness all modules needed from text handling to semantics must be there
lean formalisms linguistic theory and applications grammar development in alep
only then these ambiguities could be resolved on the basis of morphotactic intorlnation
mpro also provides a facility for searching syntactic structures in corpora
mpro provides the attachment of rich linguistic information to the words
they were investigated automatically by the non statistical mpro tagger
null efficiency is good compared to other unification based systems
optimal keys again result in a substantial gain in efficiency
NUM the post discourse meaning mo does not depend on the words w or the parse structure t once the pre discourse meaning ms is determined
the meaning of a sentence is determined by taking the highest scoring theory from among the n best possibilities produced by the final stage in the model
we now introduce a third independence assumption NUM the probability of words w does not depend on meaning ms given that parse tis known
finally we assume that most of the probability mass for each discourse dependent meaning is focused on a single parse tree and on a single pre discourse meaning
the semantic syntactic character of this representation offers several advantages NUM annotation well founded syntactic principles provide a framework for designing an organized and consistent annotation schema
this treatment will be inve stigated in section NUM NUM
this work was supported by the advanced research projects agency and monitored by the office of naval research under contract no n00014 NUM c NUM and by ft
to compute p t i ft each of the state transitions from the previous parsing model are simply rescored conditioned on the frame type
the software layer restricts and adapts the view just like the sql views the programs have on the information of a lexical entry
a linguistic restriction can be exl rcssed in terms of feature value pairs which in turn can be represented as a l ag
most of ttle t ls NUM attain a 5probably the mswer will be different depending on tile task of tile t l pure tagging or auxiliary function for the parser
the lexical database for dutch was built using several resources an existing electronic valency dictionary NUM and a list of words extracted from a medical corpus cardiology patient discharge summaries
this methods allows that all sorts of information can be coupled to a lexical entry in the database while only the information relevant for a specific nlp application passes the software filter
the t roblem with an application of this type is the trade olr between overkill a good analysis is injustly discarded and undershoot an invalid analysis is kept
in the same way ironic environments of examples NUM NUM fall in type NUM type NUM respectively and that of example NUM falls in type NUM
NUM hnplications of the theory distinction between ironic and non ironic utterances our theory an disl inguish iron utterances from non iron ones
the remaining ambiguity is hard to resolve fully safely but probabilistic and hcnristic techniques are likely to still improve tile pertbrmance
the maximal context in the present application is a sentence but there is a need for extending it over sentence boundaries
select ncl NUM NUM pl of the word wa if in the preceding cohort there is a feature belonging to the set ncl NUM
the cgp of swahili has presently a total of NUM rules in four different sections for disambiguation and NUM rules for syntactic mapping
null the most complicated part of the lexicon is the description of verb forms which requires a total of NUM sub lexicons
mbele ya mbele ya in front of and the same constructions are written into the lexicon with corresponding analysis
certain types of rules should be applied first without giving a possibility to other less clearly stated rules to interfere
noun roots are located in NUM separate sublexicons and access to them is permitted from the corresponding class prefix es
the problem tackled here is to develop an efficient training corpus
estimating probabilities on NUM genotypes rather than NUM NUM words is an enormous gain
using word class for part of speech disambiguation evelyne tzoukermann and dragomir r radev
the final section offers a methodology for developing an adequate training corpus
there are several facts which demonstrate the power of genotypes for disambiguation
NUM capture contextual probabilities genotypes must be considered in context
that allows us to make predictions for words missing from the training corpus
the tagger is based on a tagset of NUM parts of speech
moreover the estimation is achieved on a sequence of n gram genotypes
figure NUM number of words per ambiguity level in two different corpora
the multimedia grammar is demonstrated in the generation of the phrase the fifth character in this line with highlighting of a specified line as given in figure NUM the domain is pascal tutoring and the phrase specifies a particular character that the system wishes to comment on
first elaboration is exploited to describe domain actions states or objects in a piecemeal fashion
in partieular we found that these specifications could be readily transformed into a form appropriate for the knowledge base required by a text generation system such as draftei
more loosely speaking rewriting s has to terminate after a finite number of steps with probability NUM or else the grammar is inconsistent
all measurements were obtained on a sun sparcstation NUM with a commonlisp clos implementation of generic sparse matrices that was not particularly optimized for this task
implicit communication of three properties is accomplished in such a way that an utterance alludes to the speaker s expectation violates pragmatic principles and implies the speaker s emotional attitude
using this extension we obtained an recognition of about NUM after a new trial run
an examination of the few errors three samples in the induced flapping and three rule transducers points out a flaw in our model
the surface string was generated from each underlying form by mechanically applying the one or more rules we were attempting to induce in each experiment
it has special provisions for treating text in the form of records
NUM NUM millionen dollar sechsundzwanzig millim den d mark
we ewduated pragmatic constraints in an utterance simulation experiment where discourses generate d
from the agenda and added to the database and then the next formula is taken from the agenda and so on until the agenda is empty
for example this method will simplify the higher order formula xo yo z to become xo y generating an additional assumption of z
imagine a prolog implementation of the method with indexed fornmlae being stored as facts edges in the proh g database
xo yo yo z yo z z y yo yo z x xo z
it can be seen that indexing in the above method plays a role sinfilar to that of spans within standard hart parsing
the sequent ix proven ij g u a fi r some assigmnent of a vahle NUM o a
the introduction rule discharges precisely one assumption b within the proof to which it applies ensuring linear use of resources i.e. that each resource is used precisely once
to implement this i have incorporated the notion of discourse center together with the me hanism of center shift into a dynamic sysrein
this set of brackets does not contain those ones used for the replacement i i because if we later check for them we do not want this check to be always satisfied but only when the specified contexts are present in order to be able to confirm or to cancel the replacement a posteriori
null before tile replacement we make the following three transformations NUM complex regular expressions like NUM are transformed into elementary ones like NUM where every single replacement consists of only one ui i er one lower one lei t and one right expression
the first step can be described as follows e n t suff NUM tag the first transducer in NUM inserts the tags of the third person plural present indicative between the word and the tags of the actually required subjunctive form
jon also onlgt read the letters lhat 5ue e sent to paul
quantifier raising may not apply to quanti null tiers occurring in a scope island
comparison with rooth and krifka under the alternative semantics approach soes are captured as follows
but now consider the following example NUM a don only likes mary
krifka claims that quasi soes have prosodically marked loci and thus do not raise any specific difficulty
assuming the semantic of only given above tke semantic representation of la is then
for computing the focus semantic value we propose to use t igher order unification
this means that our approach inherits the adwmtages of kratzer s approach c
moreover NUM itself is a special instance of our above observation that a more salient content should be referred to by a lighter message provided that the backwardlooking center is particularly salient
from the psycholinguistic perspective this can be explained in the following way the former interpretation has a stronger lexical preference than the latter and thus is to be preferred according to lpr
on reversing the generation process in optimality theory
if the check on em is not performed as we suggest emr will be NUM
at random when ipi i1 plex is and ipsyn i1 psyn is NUM
figure NUM scoring of grumadwet by nocoda
we have had contact with michael elhadad about the possibility of implementing machinery in fuf that will allow the kind of dynamic ordering we require
figure NUM regular language generated by nocoda
figure NUM align prefix fst regular language
nocoda penalizes syllables with coda consonants
the harmony marks include two non harmonicmarks i.e.
as the rules have access to all information about the fragments they consider this makes it possible to control the parsing process effectively enough depending on the specific situation in the sentence
for the set of fragments constructed the degree of disconnectedness c is counted as the least number of fragments covering all words of the sentence
faster performance may be expected when the granunar is enlarged because the proportion of sentences with syntss in comparison with quasi correct ones will become higher
the pos tagset can be described as follows
and for each binary noun partition we have two different subsets a special case of which is when one subset is a r and the other the empty set NUM the number of possible binary noml partitions is 2taq NUM NUM l j
thus l reiundvierzig milliarden l ollm is matched by the pattern measure see above and is replaced by the sgml markup usr note that tile matched sequence is copied into the attribute val and that in the data content spaces are replaced by underscores
lip ll et ho chtst ritlg w wds has d cqt th mi l t ritlciph is h reficalty sc rtd
to estimale a model clustering words and measured the i l distancd between deg l he k distance relative clt l opy which is widely used in information theory and sta tist ics is a nleasur NUM of dista n c l wcen two distributions
the models selected by mdl converge to the true model approximately at the rate of NUM s where s is the nmnber of parameters in the true model whereas for mle the rate is l t where t is the size of the domain or in our context the total number of elements of n x v consistency is another desirable property of mdl which is not shared by mle
looking at the lexical representation of causative verbs in NUM and the examples NUM and NUM it is easy to check that principle a is satisfied in the lower arg s list for the binding ziroo himself where ziroo is the subject and in the upper arg s for the binding taroo himself where taroo is now the subject
the maria talked about of the new director with the pedro mary talked about the new director to pedro given the linear order for the arg s value the current theory assumes it is predicted that if a reflexive occurring as the oblique complement y is grammatically bound by an antecedent occurring as the oblique complement x then x is less oblique than y
consequently this solution relies also on the three basic assumptions adopted fbr the analysis of toba batak reflexives i the principles of binding theory remain invariant ii a new list of subcategorized elements termed arg s is adopted iii o command relations are defined on the basis of the obliqueness hierarchy established in this new list
in particular on a par with its segmentation into sub lists and its splillin4 into possibly different ot liqueness hierarchies a branch in g ol liqueness ordering should be also admitted
as to the japanese reflexive zibun like english reflexives it must be locally o bound with some particulars as for instance its being subjectoriented that is it can be bound only by a subject
the first case is that verbs in the matrix clauses are in volitional use
once the binding obliqueness is unpacked from the valence list and gets an autonomous status it becomes easier to increase the empirical adequacy of binding theory in particular and the syntax semantics accuracy in general
by employing models that embody the assumption that words belonging to a same class occur in the same context with equal likelihood our method achieves the smoothing effect as a side effect of the clustering process where the domains of smoothing coincide with the classes obtained by clustering
one experiment used the el lcb corl us a s a raw corpus ignoring the pos tags in order to cal ulate recall and precision
in this section we describe the algorithm used to calcnlate tile word rneasure of all arbitrary string and tire probabilities that the string belongs to each of a set of poss
wc propose a method to extract words from a corl us and estimate the probability that each word belongs to given parts of speech poss using a distributional analysis
where f x is the frequency of the string t in the corpus and p poslot is the estimated probability that ct belongs to the pos
where there was a conflict between two or more possible matches of a string context with tire pos hash keys the longest match was selected
since all true words must belong to one or more poss the minimum value of f p can be used to decide whether a string is a word or not
where p poskla is the probability that the string a belongs to posk and d posk is tire environment of posk
the elements which precede tile type a re described by the left probability distribution and those which follow it by the right probability distribution
then for each threshold level our algorithm decided which of the candidate strings were words and assigned a pos to each instance of the word strings
our method is based on the hypothesis that sets of strings preceding or following two arbitrary words belonging to the same pos are similar to each other
in order to accurately render the full range of meaning conveyed by such utterances it is not sufficient to limit attention to syntactic and semantic aspects of spoken expressions
to solve onstrainl hierar hy we firstly lind a m ximal subsel of constraints for the strongest level which is onsistent with the require l constrmnl s
atim i NUM NUM a act e2 buy aacto r e2 he a object e2 telescope
ff a i rson gives some thing to tim other person at time i then the other l erson shouhl btve it a t time j where i j
therefore re asoning process an be un hwstoo l easily colnt ared to other lnechallisln using numerical reasoning or comt h x inferca rules
since wc use a tirst order predicate calculus for a basic language we all rot resent various kinds of inforln ttion such as grammaticsfl rules and semantical rules in one damework
however this re ding is not tinal since at least the fl llowing prcfl rences are involved in the above reading and these preferences call be teib ted
NUM NUM example english vs japanese politeness
we then used the data to estimate a class based model dendroid distribution and evaluated the estimated model by measuring the mlmber of dependencies dependency arcs it has and the kl distance between the estimated model and the true model
since a class based mode tends to have more than NUM parameters usually the current data size available in the penn tree bank is not enough for accurate estimation of the dependencies wilhin case fi antes of most verbs
our ex l crimenl al rcsull s shown in l able NUM indicate hal NUM he use o t he ndroid models can achieve up t o NUM
since nlany of the random variables case slots in case flame patterns are esseutially independent this feature is crucial in our context and we thus employ suzuki s algorithm for learning our case frame patterns
as explained in introduction the l irol lelu of learning case fraille l atteriis ca it be viewed as that of estilnating the unde rlying mulli dimemsioltal joilll distribuliot which giw s rise to such data
word senses are in general difficult to define precisely however and in language processing they would have to be disambiguated dora the context nyway which is essentially equivalent to assuming that the dependencies between case slots exist
it is not difficult t o see tha t there are NUM and only NUM such representations for the joint distribution p x1 x NUM x3 disregarding the actual nmnerical values of t he probability parameters
such thing 0bj reject thing sbj be difficult our flexible matching process is ble to map an inverted construction like example input NUM onto its normalized form NUM
pos of words are given in the data set as well as the bracketings of noun groups
we argue that these two models can be used to explain the student s sentence generation capabilities and should affect the system s response generation as well
all random choices were made with equal probabilities for the results
deviacing only in the following aspects the sub al egorization l rineil h is given in a binary bra nching fashion
instances of this pattern in the corpus NUM are no different to instances of the similar rule with a np mother and the pattern is more suited to a nominal interpretation
the choice of a more general verb for the english version is purely stylistic since a specific verb inflate exists as shown in the literal translation of gf
in the corpus denominal verbs are systematically used in the english versions when they are available even though this choice leads to bilingual pairs with quite different lexical structures
but if the same action were expressed as a verb the manner attribute would take the form of an adverbial modifier 4f nettoyer soigneusement le corps du filtre
la 2a 4a 5a lb 2c 5b NUM 4b ld 2b le 3a 3b
first irony can be comnmnicared by various expressions that do not include such violation true assertions such as 2a in figure NUM understatements such as 2c and echoic utterances such as 5a
this relation means dlat if an action a ix executed in a situation s l sut porting the infon al then it causes the inf m a2 to be trlle in the re suiting situatiol s
an mentioned earlier the last three cases have been proi lenlatic for all the previous theories of irony NUM ecause none of these theories recognized a wide varie ty of principles violated by ironic utterances
at the same time many ironic utterances make emotion elieiting rules for the speaker s attitude some of which are shown in figure NUM accessible by the hearers by alluding to one of premises of the rule
for exmnple lies and other non ironic utt wances violating the pragmatic principle do not allude to any antecedent exi ectation and or lo not offer cues fi r reasoning about the si e aker s
a situation sj is a part of a situation s2 i.e. sl NUM s2 if and only if every infon supt orted t y st is also sut ported by su
several irony theories have been proposed in the last few decades but all the theories as we will explain make the same mistake in that they confuse the two difl erent questions q1 and q2
rather than having the model select a subset of the NUM possible links as in model a and then discard the result unless each word has exactly one parent we might restrict the model to picking out one parent per word to begin with
models a and b suggest that spe kers produce text in such a way that the grammatical relations can be easily decoded by a listener given words preferences to associate with each other and tags preferences to follow each other
the first product encodes the markovian probability that the tag word pairs k through g NUM are as claimed by the span conditional on the appearance of specific tag word pairs at g NUM
this suggcsts that sut eategjo rization NUM rcferc lccs the only i lctor onsidered by model j i lay a substantial role in i he sti uclure of trcebank scntcn cs
in this paper we NUM resent a lexible l robat ilistic parser that simultaneously assigns both part ofsl eech tags and a bare bones dependency structure illustrate d in l igure NUM
each time a word i is added it generates a markov sequence of tag word pairs to serve as its left children and an separate sequence of tag word pairs as its right children
p word i i tag a pr words tags links c pr words tags preferences r words tags
this means that in i he parse of figure 3b the link price NUM will be sensitive to the fact that fell already has a ok set chihl tagged as a noun nn
whenever i is retrieved from h we process each node n in rule i as usual
as already pointed out the translation problem investigated here is closely related with the standard tree pattern matching problem
this only affects the size of ag not the time requirements of algorithm NUM
polynomial space requirements can be guaranteed if one switches to top down tree pattern matching algorithms
transition function NUM restricted to the useful states is specified in figure NUM
for each node n state is assigned a state of ag as specified above
we precede the specification of the method with an informal presentation
the correctness of algorithm NUM then follows from the definition of the heap data structure
lcb nzh rcb no other final state is associated with a node of c1
we leave the very important problem of stress assignment for later study
in turn the mp model performs better than prod in all cases
the set of candidate pronunciations is then passed to the decision function
rather than re implement a flawed algorithm we have used manually aligned data
the first is based on path length
the latter has obvious commonalities with most single route conncctionist models e.g.
the commonest problem is vowel substitution
we call this the mp model
a word error rate of NUM is reported
two other methods l or scoring have also been implemented
the process iterates until no more states remain in the queue
the queue orders states by their start indices highest first
g if we build the rightmost derivation grammar associated with a shared parse forest and we remove all its useless symbols we get a reduced cfg say d
for this liged forest the relations are the start symbol of the ldg associated with the liged forest l is s o3
one way is to consider a top down strategy the xproductions in a ldg are generated iff x is the start symbol or occurs in the rhs of an already generated production
l must contain an identical number k of productions which push 7a i.e. the production rl0 and productions which pop 7a i.e. the production r3
in this approach the number of productions and the construction time of this cfg is at worst o n6 though much better results occur in practical situations
figure NUM a topic relation getting narrow scope
even taking these pieces of information into account the scope relations both between wa and uoda and between wa and node seem to be underspecified whereas noda always has scope over node
the problem is to distinguish the discourse relations which take the wide scope relative to other scope taking elements on the one hand and to have them underspecified among each other on the other
the latter called a possible plugging fully specifies 2in tl s example each discourse relation element is taken as a predicate with the antecedent and the conclusion part as its arguments
this proposal crucially relies on the fact that for every discourse relation element which occurs in a sentence one of its two holes can be plugged by a drs in a lexically determined way
the relations of holes to labels by way of an injective plugging function from holes to labels which determines which hole is instantiat ed into by or is bound to which label
this paper is distinguished from these works in two perspectives first it concentrates on the sentential level and offers a treatment of multiple discourse relations in terms of a formalism for underspecified structures of drss
NUM if there are a number of discourse relation elements contained in a sentence the partitions they introduce can differ from each other see sec NUM
this can be observed in fig NUM
this resolution possibility corresponds to NUM
NUM finally we evaluate the c value for wall street from equation NUM we find c value y all street NUM
NUM if n a is the number of times a appears and a is not a substring of an already extracted candidate collocation then a is assigned NUM
recently large scale textual corpora give the potential of working with the real data ither fin grammar inferring or for enriching the le xicon
therefore they do not tbrm candidate collocations and they do not change the t wall street and the c wall street values
the issue is how to distinguish when a substring of a andidate ollo ation is a candidate collocation and when it is not
for each n gram b every tin le it is found ill a longer extracted n gram a the vahles t b and c b are revised
the of the wall street is not accepted as a eamtidate collocations since it apt ears with the same fl equeney as the of the wall street jom nal
more specifically if a is the length NUM of the string a its c value is analog us to la i NUM
if a has the same hequen y with a longer candidate ollocation that contains a it is assigne t c value a o i.e. is not a collocation
in l his palter we address the probl m of ne stcd collocrd ions thai is those being l art of longer colloc ttions
given the sentence the union s lawyers are reviewing the suit we would like the system to decide automatically that suit is used there in its court related sense we assume that the part of speech of the polysemous word is known
assuming that we have a symbol a representing a single segment the symbol representing a word boundary and allowing for the possibility of intervening optional stress marks which do not count as segments these two possibilities can be represented by the regular expressions for a in a of table NUM NUM at this node there is no decision based on the righthand context so the righthand context is free
and just as each tree represents a system of weighted two level rules so a set of trees e.g. where each tree deals with the realization of a particular phone represents a system of weighted two level rules where each two level rule is compiled from each of the individual trees
the performance of our system compares favorably to that of systems trained on sets larger by a factor of NUM the results described in section NUM were obtained following learning from several dozen examples in comparison to thousands of examples in other automatic methods
note that unfike the contexts of suit which may discuss either court action or clothing the contexts of court are not fikely to be especially related to clothing and similarly those of clothes will normally have tittle to do with lawsuits
where pr0d i is estimated from the frequency of w in the entire corpus and pr wi w from the frequency of i yi in the training set given the examples of the current ambiguous word rcb v cf
given the dynamic perspective the puzzle evaporates the anaphoric expression and its antecedent might represent exactly the same meaning since meaninn is fundamentally a potential to be evaluated with respect to some context
in dm resulting system the mechanism of center sh ft allows a simple elegant analysis of a variety of phenomena involving sloppy identity in ellipsis at d paycheck pronom s
i will extend musk ms system to permit anaphora involving vp s as well as np s and to allow antecedents to be dynamic ms well as ordinary extensional objects
centering draws attention to a particular role that a discourse entity can hold fl om time to time t he current center will be shifted wit h a new center
good keywords tend to bunch up into many fewer documents boycott for example bunches up into only NUM documents much less than chance d NUM rr NUM NUM NUM NUM
trivially the string duplication languages can be recognized with time complexity proportional to the length of the string if the string is of even length and its first half is identical to the second half then this can be established in just linear time
this trick can also be used for accommodating inferrable or hearer old entities that behave as if they are discourse old even though they are literally discourse new
note that the focus unlike the topic can contain more than one element this allows broad focus as well as narrow focusing
for now items in the ground are either generated in between the topic and the focus or post posed behind the verb as backgrounded information
speakers can shill loa new topic at the start of a new discourse sag ileal ts ih NUM a
the topic is the maiu element that the sentence is about and the comment is the information conveyed about this toi ic
both somewhat and boycott appeared approximately NUM times in a corpus of NUM associated press articles but boycott is a better keyword because its idf is farther from what would be expected by chance poisson
the algorithm above works for verbs since i place events that are realized as verbs in the sentence into the discourse model as well
cf list that contains every lis ours entity that is remized in thai utteraltce
amount millionenlmilliarden currency mark id mark i dollar carmeasure amount currency
if a word occurs both as a proper noun and as a non proper noun the user will be asked if he or she wants it to be tagged
first patterns for cardinals were specified e g rcb umlauted characters and if are matched by the system though they are not shown here
the types of word construct of interest here lend themselves well to identification by matching regular expressions over each input sentence considered as a record tagging them as specific instances of general phenomena e.g.
the fact that an interactive tagging tool can be so easily inte grated in to the linguistic processing system is of obvious and considerable benefit
the last two patterns described define measure being the succession of a cardinal number as a digit or a string followed by curmeasure being the concatenation of amount and currency
variables are not evaluated when they occur in quotes so quoting is ended and then restarted after the variable name whence the proliferation of double quotes within the complex patterns
so if the input is one already having pos information as the result of a corpus tagging tim th ls is the appropriate place to assure the flow of information
since this is a pre processing treatment there is no disambiguating information present and fully automatic tagging can not be preceding expression square brackets surround alternative characters possible specified as a range e.g.
tagging proper nouns presents a special problem since unlike the case of numbers and dates there is a great deal of uncertainty involved as to whether something is a proper noun or not
for example inverted word order and repetitions usually emphasize certain parts of the utterance
one strategy that is realized as fronting may be marked as intonation in another language
from the combination of the interpretations genera ted
step NUM architecture design any multi moda l
this is especially true in a nmld moda l
multi modal method a design method for building multi modal systems
NUM nigay and coutaz s tuulci ntodal system categorization
more fully and develol a simple example
the process breaks down into two stages classification tasks the irrelevant texts should reflect the types of texts that will need to be distinguished from relevant texts
none of the words or phrases in the texts need to be tagged but each text must be classified as either relevant or irrelevant to the targeted domain
for example a concept node called murder passive is triggered by the verb murdered but activated only when the verb appears in a passive construction
text classification experiments in the muc NUM terrorism domain show that the autoslog ts dictionary performs comparably to a hand crafted dictionary and actually achieves higher precision on one test set
autoslog ts demonstrates that conceptual patterns for information extraction can be acquired automatically from only a preclassified text corpus thereby obviating the need for an annotated training corpus
since autoslog ts uses statistical filtering we do n t have to worry as much about the number of concept nodes generated and therefore do n t need separate rule sets
for the same reason we did not calculate the precisions for thresholds more than NUM NUM in table NUM
this cyclic magic rule is derived from the head recursive vp rule in the example grammar
it influences neither the efficiency of processing with the grammar nor the completeness of the evaluation process
ilihey a re identical in their syntactic lltll lcb s
the structural complexity of the sentence may change as a result
our work is motivated by the goal of pragmatically high quality translation of spoken utterances of the type that may be found in human to human spoken dialogues
our theory provides a computationally feasible framework of irony as the first step toward a full fledged computational model of irony and it can account for several empirical findings fi om psycholinguistics
if al NUM fix the mow otherwise ascertain the mowe with probability p exl al t
annealing against that of one NUM ascd on m i ill hierarchical wom clust c ring
we also evaluated our method by conducting pp attachment disambiguation experiments using a thesaurus automatically constructed by it and found that disambiguation results can be improved
a method of constructing a thesaurus based on corpus data usually consists of the following three steps i extract co occurrence data e.g.
in these representations achieve p designates an action that achieves goal p notation h i l specifies a list where h is the head of the list and l is the rest
such cases happened when the system started utterantes based on an abstract domain i lan took a long time to obtain a more concrete plan and then elaborated on a route from a location that was not in focus based on the concrete plan
from the musashino center basu de mo ori no eki made ikimasu bus by nearest station to go by bus to the nearest station go for brevity we have omitted action schemata and decomposition methods for utterance planning using motivation and circumstance
in each diah gue two subjects n and e were lsked to converse by telephone to lind a solution to the l roblem of how n could get from one place to another
tal le NUM shows the frequency distribution for tim ramnlatiea ategoric s of iu where np and i p mean noun NUM hrase and NUM ostl ositional phrase
r3 phm a4 a5 a6 a71 r4 cont a4 lcb type m move source a4 xl manner a4 x2 dest a4 x7 rcb cont xt lcb type xt station named x7 kichijoji rcb
NUM the agencies hcm and dyr are hemselves joint ventures
an initial ee could then contain an anaphor of the prr type
in all other cases prrs are treated equally to other pronouns
syntactic and semantic features can easily be used to resolve these anaphors
NUM girls who he has dated say that sam is charming
this assumption is particularly apt when we dispose of a conceptual representation
it coordinates the treat null ment of different kinds of ambiguities
when dealing with ambiguities another important aspect is managing multiple readings
as stated above embedded sentences include several elementary events ees
null c after lheir game alfred and zohar had ice cream cones
agreement on subjects nominative nps and finite form verbs vps excluding the be verb is disjunctively specified as
while this kind of decomposition has proven useful for practical purposes dividing the process into separate components increased the control it has also encouraged researchers to build into their systems wrong assumptions content is generally determined in one go one shot process and information flow is one directional going downwards from the conceptual level to the linguistic level
suppose we warned to express the fact that a person moves on a given surlhce in a given direction see gi in figure NUM next page or the left branch of second event of our example in section NUM on message phmning he went to his canoe NUM obviously there is more than one way
my contribution in this paper consists in providing evidence for the following three claims a thought is underspecified at the onset of lexicalization b language can feed back on thought i.e. words can specify the conceptual component c our mental dictionaries are the interface between language and thought
while there are many ways to perform this task i believe that people do this in the following way in particular if the message is going to be long first an outline is planned global or skeleton planning which is then filled in with details local planning elaboration
for example l is everything related to meaning processed entirely and once and for all hence messages can neither be changed not be refined or NUM are the objects to be talked about only specified to the degree to which they need to be at this stage of the process limited commitment planning
a major step in natural language generation nlg consists in choosing content words for expressing the planned message
i believe that it does provided that the additional information speed is consistent with some belief about the state of the world and that the speaker considers it worthwhile mentioning if that is so then we have here evidence for feedback of the lexical component to the conceptual component
if my view is correct this could have implications on the design of generation architectures instead of separating message planning and realization viewing the process as being strictly sequential we could allow for feedback loops interleaved process whereby the linguistic component could feed back to the conceptual component
put differently why cam at that stage about the specificity of a word or a relyrent how great a detail to give in order to ch u aclcrizc an object if we areffl even sum whether the planned message contains all and only the inlornmtion we wish to convey
in this model the speaker first selects an example e that is closest to the core of the message that she intends to express
they are not found in dictionaries are very large in number come and go every day and appear in many alias forms
efficiency is the major reason as underspecified syntax rules are very inefficient
in as adjunct the preposition puts its content on a restriction list
conlpleteness all modules needed from text handling to semantics had to ve developed
the formalism for lingware development is lean but it provides sufficient means to support mainstream felicitous linguistic descriptions
this section shows an example of the manner in which an expression containing a pragmatic politeness component is translated from japanese to english
in the case of earley s parser there is a simple extension to
a version that collapses all such chains of productions is given below
NUM unfortunately the terminology used in the literature is not uniform
apart from input length complexity is also determined by grammar size
definition NUM the following definitions are relative to an implied input string x
the two steps of this algorithm can be briefly characterized as follows
i.e. the samples are assumed to be distributed identically and independently
computational linguistics volume NUM number NUM NUM NUM earley paths and their probabilities
context freeness in a probabilistic setting translates into conditional independence of rule choices
having ensured correct concatenation we delete all marked classes on the upper side of the relation by means of and all marked tags on the lower side by means of by composing the above relations with the preliminary sentence model we obtain the final sentence models
s nl fst 20k f1 NUM NUM s nl fst 50k f1 NUM NUM s nl fst 100k f1 NUM NUM s nl fst 100k f2 NUM NUM s nl fst 100k f4 s nl fst 100k f8
assuming that the correct partial parse is a function of the word prefix it makes sense to compare the word level perplexity pp of a standard n gram lm with that of the p wk wk itk NUM model
therefore a naturally occurring japanese sentence can be considered to consist of NUM NUM NUM NUM simple sentences on average
the set of cfs is ordered by their grammatical properties which are considered to reflect their degrees of salience
theory uses the results of the zero pronolm resolution in previous sentences error cha ining
it di lk rs koln tile original centering algorithm in the following two t oints
these sentences are partitioned into the folh wing simple sentences he did not notice ilim
they try to establish coherence relations by the costly inference while we use only the surface information
the cb of the sentence is computed as the side effect of performing the zero pronoun resolution
therefore we give these noun phrases the least t reference as the antecedents although the
we inlplement two versions of our zero pronoui resolution systems which are based on two versions of the centering algorithms teat are inentioned in sect oil two respectively and ewfluate the imrl orina nce by comparing ours with the perfl rmanee of the original ee ntering algorithms
t tke into a ount the information of mjunctiv
the a rl icles were morld h gi tally analyzed a utoma tica lly
co expand the vocabulary of the thesaurus it is important to position new words in it automati ally
word in isamap is represented by a pair of capital rom tn letters and the word s english tr mslatlon
the nodes have direct is a relationships in other words the nodes are c an be connected in the hieraredy of nodes
for example the likely area of heavy oir is i jeet lnaterla l
l his sectio descrilms the procedure fi r positioning words iu samai
third ea ch node in the thesaurus has viewpoi nts that distinguish it from ol her nodes
first for each node in the 1samap the slmil rity between the word and the node is cmculated
the contribution of this factor is reciprocally related to the normalized distance
we thank dan roth for comments on a draft of this paper
we address this problem in several ways
learning similarity based word sense disambiguation from sparse data
second we extend the training set by adding examples of related words
we will use this observation to tag the original contexts of suic
intuitively the transitive exploration of similarities is exhausted after three iterations
these tagged examples were then used as seed examples in the bootstrapping process
on the one hand the sense distinctions made by wordnet NUM NUM arc not always satisl actory
on tire other hand our algorithm is not designed to work on the file level e.g.
the figure also shows the guessing baseline given hy selecting senses at random
its application for automatic spelling correction is outlined in tagirre ct al NUM
an experiment using bilingual patent specification corpora achieved NUM recall and NUM precision this demonstrates that the method effectively reduces the cost of bilingual dictionary augmentation
in case b the pseudo recall and precision before feedback were NUM NUM and NUM NUM respectively and those after feedback were NUM NUM and NUM NUM
in sec NUM we describe the technical details and in sec NUM we describe an experiment using patent specification texts
to increase tile reliability of the correlation values we remove tile useless words from tile co occurrence sets before calculating the correlations
even if a correspondence pair of words fails to be extracted from one bilingual document it may be extracted from another bilingual document where it occurs prevailingly
in contrast that of the previous linguistic methods is to associate a pair of compound words through their constituent word information with the assistance of a bilingual dictionary
one of the major problems with bilingual dictionaries is that they are expensive to build since a huge number of terms are used in a variety of fields
we implemented our proposed method on a workstation and carried out an experiment using patent specification documents in japanese and english and a bilingual dictionary for a machine translation system
definitions of co occurrence include syntactic co occurrence co occurrence in a k word window co occurrcuce ill a sentence and co occnrfcncc ill a documen
table NUM shows results which indicate how different parts of the system contribute to performance
if no parse is found the threshold is lowered and parsing is attempted again
this section describes two modifications which im null prove the model s performance
the probability of a parse tree t given a sentence s is
this suggests that distance is a crucial variable when deciding whether two words are related
the method uses lexical information directly by modeling head modifier NUM relations between pairs of words
head words propagate up through the tree each parent receiving its head word from its head child
the distance measure could be extended to capture more context such as other words or tags in the sentence
they are less likely to modify a more distant verb such as escaped
approach 2b has received much attention recently
this search process is facilitated based on the probability that a word belongs to a given word class
this pruning is fully interleaved with the parsing process
cooccurring antonyms are also frequently joined by and or or or appear in noun phrases joined by prepositions and having the same head noun it was pitiful to see the thin ranks of warriors old and young
it assumes demonstratives that fighter to be anaphoric and attempts to resolve other definite references the fighters first as anaphoric and then as universal
the system can also interpret np sentence fragments as followup commands or queries by substituting the np into the semantically relevant slot of the prior utterance s logical form
thus some nouns can be disambiguated by relatively narrowly defined semantic classes such as military industrial equipment and with high reliability leading to something close to lexicalized noun phrases e.g. light cruiser or light industry
after that the nodes in the first set are in turn partitioned according to the above criterion and the process is iterated until all the matching nodes have been considered for application of r
this representation reflects the fact that in the context of an angioplasty segment ii is considered from the point of view of the physical artery segment the angioplasty is to act upon instead of the spatial notion segment ii expresses
oh j hrtery seg t i ires stenosis inclusion angioplasty mistie on the other hand the systern is in an ilu oml lete state of develolltnent
the main domain knowledge elements consist of the domain ontology fig NUM which is a subsumption hierarchy of concept types henceforth simply types and of relation types and of a set of reference models attached to the main types
NUM angioplasty of mr x NUM inchlsion angioplasty angiophtstyl p rported ml j ar tery2qegme t part llumanateing l NUM angioi lasty of a stenosis NUM a ngiot lasty purported
it then endeavors to link them that is to find a concept level relation between their two head concepts c1 and c2 that first is compatible with the semantic preferences of grammaticm relation gr and second conforms to the representational canon made of the reference models
artery segment rcb zone of spatial o bjcct spatial role segment ai into angioplasty purported obj art cry segment NUM zone of spatial object spatial role segment i i
tracted verb co occurring with a noun is v given that the noun belongs to word class c
as explained above the first solution can produce an unbounded number of trees
all the probabilities in NUM can be estimated from training data based on the following equations
t yo a and furthermore allow only atomic goals i.e.
wil h labels under a sl e eiti d
then a loop is followed in which a formula is taken
an adequate algorithm for use with the above approach is easily stated
the solution to this problem relies upon the indexing method adopted above
we consider only the implicational fragment of intuitionistic linear logic
the prescnl work contrilml es to this pro ect
where the transducer n is first converted into llevel format NUM then composed with the filter fs eq
he defined the class code of each kanzi character to the code of words including only that kanzi
another approach would be to construct a hierarchy from a set of words of each class using a clustering algorithm
rcb where or means that th e modifiee of word is the first occurrence to the left or right of word or means modifiee is the second occurrence to the left or right of word
we assum e that the values of wi s and bi s are defined such that a template element frame for an organization or a person consists of all the relevant information such as locale and alias about the organization or the person mentioned in the text
the greatest limiting factor in the development of pie is the amount of time and human resources available to examine training articles to create domain specific vocabulary and dependency patterns and to inspect the differences between the answer keys and the system responses to fine tune the system
if all the four components in the pattern in NUM are matched by a sequence of lexical items or words the action part of the rule will be executed lexical items that span on a subsequence of the match will be deleted
it contains an interpreter for lisp like expressions so that all the knowledge structures such a s finite automata for finding sentence boundaries the lexicon the lexical rules the grammar network an d extraction rules are written in lisp like expressions
the rule NUM detects sequences of capitalized words that are not recognized as any of the above categories it then calls the function fund prev occ to find whether or not this sequence of words is a substring of a previously recognized proper noun
NUM kim wanted john to leave in a dependency tree NUM every word in the sentence is a modifier of exactly one other word called its hea d or modifiee except the head word of the sentence
other s are extracted from machine readable versions of oxford advanced learner s dictionary and collins englis h dictionary both from the oxford text archive and public domain proper name lists from consortium fo r lexical research at new mexico state university
we have mentioned the locative extension above its characteristic is that configuration a of the verb conveys that something is performed in a complete or holistic manner whereas configuration b lacks this facet of meaning
denotation i t not tion i denotation e co n x connotatiot connotatior colmolation i partial partial partial c semspec semspec i semspec NUM alternation altffnafio alternations n morphsyn morphsynt i morphsynt generation
he also pointed out that there are different opinions on the type of entities that are subject to a verb s valency requirements different authors describe them by syntactic class some by semantic deep cases and some by their fimction subject object etc
concerning the linguistic realizations penman and the um in their present form essentially go back to the tesnb rian suggestion that participants are realized as nominal groups with some obvious exceptions as in say that x and circumstances as prepositional phrases or as adverbs
these rules extend the denotation of a verb and rewrite its psemspec in parallel to reflect the change in valency the result is a new verbalization option which can lifter from the previous one in terms of coverage or attribution of salience not discussed here
however the same sentence employing a different intonation could be part of a clarification dialogue where the system wants to reassure that it got the user s request right
NUM this implies that choices underlying the realization of intonation may be organized in exactly the same way as other choices in the grammar see NUM NUM
in terms of semantic choices it is initiating a new embedded exchange while it is responding to a user move in the embedding exchange
is giving information and as mood and key fea future empirical studies will determine whether these alell systems indicate refinement of the previous choice
so in information seeking dialogues the type of move largely constrains the selection of speech function but it only partially constrains the mapping of speech time ion and mood
consider the following example taken from one of the information seeking dialogues the computer has retrieved an answer to a query and this answer is presented graphically to the user
further we show how competent choices at the semantic stratum guide the selection of features in the mood and key systems which finally result in the assignment of a tone
moreover with text to speech systems where the syntactic structure has to be reconstructed from the written text by means of a syntactic analysis the resulting data is seldom complete nor unambiguous
we will then propose an organization of these different parameters in terms of stratification that allows for the necessary flexibility and brigdes the gap between the dialogue model and the generator
choices in mood and key systems can often not be made unless we have access to additional knowl null edge sources as for instance a confidence measure
n type transducers have deterministic states only
finite state transducers approximating hidden markov models
NUM NUM a completed with nl type sec
t12 and t2s may refer to the same tag
all states are final marked by double circles
in our amtlysis of omlmter manuals most nouns were repeated with the same expressions unless they were repla ed
thus in NUM in figure NUM do the following is added NUM ecause a verb phrase follows this sentence
if verb phrases follow do the following is added and if noun l hrases folh w the following is added
NUM tion and correlation as might be expected the types of variables give a lot of information about the structure of the elements of the report NUM NUM NUM
by default the system assumes that all user goals are equivalent but the user can choose to change their relative weights in the input to assure that some of them are better expressed by the system
mackinlay s algorithm as used in apt takes as input a set of typed variables and determines the most efficient graphical encoding position length color for each of them
as we can see in figures NUM and NUM the same data can be expressed in very different ways according to the message the writer wishes to transmit
the captions show the name of the c bema and the intentions used to generate each figure with a quality factor NUM NUM for each intention
the captions of the figures were translated from the french output of postgraphe but the internal labels and the text produced by pr4texte figure NUM were left in french
1his method is easy to implement in a spreadsheet and some control is maintained without having to abandon automatic calculation of keys useful for large partially unknown data sets
in order to achieve our objectives we have considered the writer s goals the types and values of the variables to be presented and the relations between these variables
to solve this problem NUM optional informations are specified in the input a list of variables that can be used as keys and a list of variables that can not be used as keys
the main message is one of evolution in figure NUM graph tgv5 and correlation in figure NUM but both graphs also NUM transmit with lower efficiency the main message of the other graph
i know who i ask for them
towards these ends other corpora in the penn treebank will be examined with vpeal
as we see feaspar is better than the lr parser in all six comparison perforinance measures made
the chunker networks are only connected to the syntactic microfeatures because chunking is a syntactic task
for each feature there is a network which finds one or zero atomic values
l NUM aspar is based on a principle of chunks their features and relations
an automatie ambiguity checker warns if similar words or phrases map to ambiguous lexical feature values
null syntactic and semantic microfeatures are represented for each word as a vector of binary vahles
NUM label each chuck with feature pairs and feature relations
the feaspar architecture consists of two n tajor parts a neural network collection and a search
the linguistic feature labeler attaches features and atomic feature values if applicable to these chunks
in total there are four levels of chunks word numbers phrases clauses and sentence
second a functional perspective offers an advantage for multilingual text generation because of its ability to achieve a level of linguistic description which holds across languages more effectively than do structurally based accounts
this does not pose any problem for lexas since lexas only requires that there be a division of senses into different classes regardless of how the sense classes are defined or numbered
since we use random selection of test sentences the proportion of each sense in our test set is also approximately equal to their proportion in the whole data set in our random trials
lexas achieves a higher accuracy on the common data set and performs better than the most frequent heuristic on the highly ambiguous words in the large corpus tagged with the refined senses of wordnet
lexas follows this convention by first converting each word in an input sentence into its morphological root using the morphological analyzer of word net before assigning the appropriate word sense to the root form
we draw our sentences containing the occurrences of the NUM words listed above from the combined corpus of the NUM million word brown corpus and the NUM NUM million word wall street journal wsj corpus
mood modality and polarity to find out the extent to which actions are presented to the reader as being desirable possible mandatory or prohibited
the counts were all done using the local mean that is the feature count is divided by the total number of codings which select that feature s system
the work presented here is informing the development of our text generator by specifying the necessary coverage of the french grammar to be implemented the required discourse structures and the mechanisms needed to control them
null the analysis so far demonstrates that genre like task structure provides some measure of control over the linguistic resources but that neither of these alone is sufficient to drive a generation system
table NUM evaluation on a large data set
we reproduced in table NUM the results of past work as well as the classification accuracy of lexas which is NUM NUM with a standard deviation of NUM NUM over NUM random trials
for this reason their semantics are non truth conditional
there is evidence to suggest that this method can improve recognition performance e.g.
speed and throughput of searches through the fdf hardware search engine was measured using a commercial fdf NUM system a single fdf NUM produced a search rate of around NUM NUM mb s which could be obtained while searching NUM to NUM average queries simultaneously
table NUM correct accuracy and perplexity of the language models
r NUM to vp as pfted obj pat would be such an atomic classification
the results have to be interpreted cautiously since they are not based on the exact same sentences and detail of bracketing
compared with standard statistical methods our system relies on deeper analysis and more supervision but radically fewer examples
to make good parse decisions a wide range of features at various degrees of abstraction have to be considered
3if a feature is not defined in a specific parse state the feature interpreter assigns the special value unavailable
together with the recorded parse actions these feature vectors form parse examples that serve as input to the learning unit
values near NUM NUM or NUM NUM indicate very strong correlation whereas values near NUM NUM indicate a weak or no correlation
besides our system contex we tested three commercial systems logos systr an and globalink
at least for the very first sentence the supervisor actually has to type in the entire parse action sequence
initially the parse stack is empty and the input list contains the primitive frames produced by the morphological analyzer
NUM NUM exclamation act class exclamation
the final two systems are act com
we will take the latter first
NUM NUM inform act class inform
there is a good reason for this
the network operates in the following manner
if the technique is working perfectly the mean rank should be NUM
it is therefore possible to use the sgml tags to identify suitable texts
therefore all the patterns were checked against the original corpus to recover the original sentences
he sentences for patterns with low incidence and those whose correetne ss
analogical matching and transfer is applied recursively to the input syntactic tree
possibly developed through all inl erac tion with the user t o obtain str mg r lcb stllts for some l cgories of concci rcb l s
in general we take sw t e NUM as a piece of positive evidence and sw t e as a piece of negative evidence as provided by item t
we may also nol ice that some misclassifications due to all iml ext e t t seed e.g. see the first dip in t re ision NUM the NUM rcb todllt l s chart all ill t aet t e corrected in further t rcb ootstrapping loops
this infortnation should be in a form of a typical lexical context in which tile entities to be spotted occur e.g. the name ends with co or to the right of produced or made or to the right of maker of and so forth or simply by listing or highlighting a number of examples in text
to pick out henry kauf mmn co rand gabelli iamds inc as seeds we proceed to find new evidence in the training corlms using an unsul ervised lemrning process mnd discover thmt chmirman of rand t residcnt of rare very likely to precede coral any nalnes
hc cmtcgorization of a lexical token its belonging l o m p ivell selnalltic clmss is based llpott t rcb l information provided by the words occurriug in NUM he token itself ms well as the words thml l re cede mm follow it in t xl
at the same time the set of errors a student makes changes so too does the set of constructions a student uses appropriately
in this section we introduce a component which attempts to capture aspects of the second language acquisition process that affect the text the student is generating
while the overall system and its design encompass many important generation questions we will focus on issues involving the language model and the acquisition model
this is particularly important for the tutor we are developing since a lack of understandable input feedback is a serious problem for the deaf community
if the system contained only this model then it would predict that the student would make all mistakes invited by language transfer at every possibility
finally the system s responses are presented to the student who then has an opportunity to enter corrections to the text and have it re checked
remember that the variable speechact requestprice is object is instantiated with the semantic representation of the description as uttered by the user the variable speechact requestprice 0r object is instantiated with one object that is adequately described by the description and lcb speechact requestprice rcb 0r object is instantiated with the underspecified representation of all objects fitting the description
example NUM example NUM disjunct NUM name i addr i sti teetname tador i strzetnum disjunct NUM namej iaddr streetname disjunct NUM name addk i streetname tador i streetnum a NUM do you mean carnegie museum of natural history andy warhol museum or fort pitt museum
an example for an application specific predicate is draw string xstring xint xint whose purpose it is to draw the icon given by the second argument into the window given by the first argument at the position that is identified by the third and the fourth arguments
depending on the speech act type the path is an object of there may be subsequent rules that may perform complementary operations on the data such as calculating the path length or travel time generating a path description or highlighting the street segments belonging to the path
to illustrate the behavior of the rules we show the complete dialogue add path dst isundefined obj path NUM dst settclvar textl where do you want to go today
figure NUM two level distortion model of spoken
table NUM summarizes the part of this research was conducted when the author was on leave at mit ai la b time sec
the f measure for st in formal testing is significantly lower than that of training tests because the recall dropped from about NUM to NUM
the use of abbreviations not only results in simpler and more readable lexicon files but also make s the modifications to the entries much easier
once the post filler database has been created by the above three rules the st templates are filled b y examining the records in the database
the nuba system did not have a parser and relies on an abductive reasoner to construct the semantic relationships between domain specifi c concepts mentioned in a sentence
NUM post ceo holder john smith org xyz inc status out post president holder john smith org xyz inc
the first component matches a word that is made up of a sequence of digit s or a lexical item that has the semantic feature number
most of the information extracted are directly read off the parser outputs by a subtree pattern matcher bypassin g the usual step of constructing semantic representations
dooner jr the agency s president and chief operating officer is quit e telling NUM he will be succeeded by mr
NUM NUM NUM the time to run the muc NUM formal tests NUM articles for ne and co NUM article s for te and st on a pentium NUM pc with 24mb memory
in our example unfurling the direct cycle by replacing NUM q14 q14 with lcb NUM q14 q14 NUM q14 q14 NUM q14 q19 rcb would allow pruning of the cyclic transition NUM q14 q14 and the transition NUM q14 q19
in such a predicative lexical rule which we only note as an example and not as a linguistic proposal the subtype of the head object undergoing the rule as well as the value of the features only appropriate for the subtypes of substantive either is lost or must be specified by a separate rule for each of the subtypes
so for the lexical rule NUM the frame specification is taken care of by extending the predicate in figure NUM with a call to a frame predicate as shown in figure NUM NUM on the basis of the lexical rule specification and the signature the compiler deduces the frame predicates without requiring additional specifications by the linguist
the structure of the paper is as follows we start with a brief introduction of the formal background on which our approach is based in section NUM we then describe section NUM how lexical rules and their interaction can be encoded in a definite clause encoding that expresses systematic covariation in lexical entries
to evaluate the time efficiency of the covariation encoding we compared the parse times for our test grammar with three different computational encodings of the lexicon meurers and minnen covariation approach to hpsg lexical rules the expanded out lexicon the basic covariation encoding and the covariation encoding improved by constraint propagation
NUM association for computational linguistics computational linguistics volume NUM number NUM treatments of lexical rules as unary phrase structure rules also require their fully explicit specification which entails the last problem mentioned above
with respect to frame specification this means that there can be lexical entries such as the one in figure NUM for which we need to make sure that tl as the value of c gets transferred
this can be neglected in the present discussion but will be taken up again below
side effects the lisp code under side effects is a function whose value is ignored
suppose for simplicity that there are only NUM possible case slots random variables corresponding respectively to the subject direct object front phrase and to phrase
size most cas slot cam oi i degermin xl i o i c dep m h tt with a uy significance
sound way NUM o learn ill n ihosc pcq dcncies thai arc sl al isticmly signilicant in ill given al a
this is an empirical finding that is worth noting since up to now the independence assumption was based somy on hu null man intuit ion to the best of our knowledge
joint disl ribution is exponential if we allow n ary dependencies in general it is int asible to accurately esi itllate them with high accuracy with a data size available in practice
i am grateful to graeme htrst for the invaluable
if there are no indices the information belongs to all feature structures
indices behind types identify the typed feature structure to which this information belongs
figure NUM the rs tree of mammal weight built by the
in this case every string that is in the upper side language of the relation is mapped to an infinite set of strings in the lower side language as the upper side string can be considered as a concatenation of empty and non empty substrings with e at any position and in any number
the generalized version permits such strategies
figure NUM translation accuracy percentage correct
we introduce a polynomial time algorithm for statistical machine translation
figure NUM number of legal word alignments between
our prosperity and stability underpin our way of life
there is no significant difference in the accuracy
also of course we are dealing with
as stated earlier the normal form theorem guarantees that the same set of shapes will be explored by our search algorithm regardless of whether a binary branching btg or an arbitrary btg is used
however the order of magnitude improvements are immediately apparent
this method of incorporating dictionary information seems simpler than the method proposed by brown et ai
more accurate models can be induced by taking into account various features of the linked tokens
this relatively simple two class model linked word tokens in parallel texts as accurately as other translation models in the literature despite being trained on only one fifth as much data
of the candidate news stories NUM have been at least partly successfiflly translated at least p u fly metals that somethnes the translation was incomplete due e.g. to the difficulty of instantiating correctly some binding structures
the fund unental principle employed is then to transform tile original query into one or more different queries which unlike trmisfonned queries in a database context are not strictly equivalent but only semantically closc to the original one
the enumerative component of nkrl concerns the formal representation of the instances concrete countable ex unples see lucy wardrobe l taxi NUM of the concepts of h class their formal representations take the name of individuals
as naz enko remarks being flushed is not the cause of having a fever but that of an implicit enunciative situation where we claim affirm assert etc that someone has a fever
it can also bc utilised to support a wide range of industrial applications like populating large knowledge bases which can support thereafter all sort of intelligent applications advanced expert systems case based reasoning intelligent information retrieval etc
nkrl s transformations deal with the problem of obtaining a plausible answer from a database of factual occurrences also in the absence of the explicitly requested infommlion by searching semantic affinities between what is requested and what is really present in file base
other modulators are the temporal modulators begin end obs erve see also fig NUM modulators work as global operators which take as their argument tile whole predicative template or occurrence
the definitional component supplies the nkrl representations called concepts of all the general notions like physical entity human being taxi city etc which can play the role of arguments within the data su uctures of the two components above
theh function is that of capturing during the match between file antecedents and the results of the syntactic specialist nl or h class terms to be then used as specialisation terms lot filling up the activated templates and building the final nkrl structures
for grammars of bounded ambiguity the incremental per word cost reduces to o l NUM NUM total
what is the probability that x occurs as a prefix of some string generated by g the prefix probability of x
this is consistent with the interpretation that the initial state is derived from a dummy production s for which no alternatives exist
an earley parser can be minimally modified to take advantage of bracketed strings by invoking itself recursively when a left parenthesis is encountered
we describe reverse transitions using the same notation as for their forward counterparts annotating each state with its outer and inner probabilities
in these sums each p corresponds to a choice of the first production each q to a choice of the second production
a prerequisite for this approach is to precompute for all nonterminals x the probability that x expands to the empty string
finally section NUM details our efforts to radically expand the size of our training corpus by employing techniques of treebank conversion
where si denotes the syntactic preference value of the ith attachment in the syntactic tree of interpretation i and m the number of attachments in it
therefore when we use only the syntactic likelihood to perform disambiguation we can expect the former interpretation in figure NUM b to be preferred
we used these data to estimate three word probabilities and two word probabilities furthermore we extracted NUM sentences from the wsj tagged corpus of the penn tree bank
itowever in extracting the meaning of an inlmt sentence many default values are required so as to execute heuristic inferences
the ebmt method prepares a large number of translation examples the translation example that most closely matches the input expression is retrieved and tile example is nfimicked
moreover if there are double topic markers in a sentence they can not i e replaced by other particles NUM
this model is in a sense driven by transfer and we call it transfer driven machine anslation tdmt
NUM handling euphemistic expressions under the influence of social position or situation euphemistic expressions appear in various scenes in various forms
also we are planning to integrate speech recognition with ti m f for achieving effective and efficient speech translation
iii yoyaku wo ten alphabetically and surrounded by double quotes and the corresponding english words with usage modifiers follow in parenthesis
through je and jk implementation we believe that the translation of every language pair can be achieved in the same framework using tdmt
we have applied tdmt to two language pairs i.e. japanese english and japanese korean as a first step toward multi lingual translation
such a method may work well in a certain domain but less scalability may be revealed when making a larger prototype system
the process of pruning the decision trees is complicated by the fact that the pruning operations allowed at one state depend on the status of the trees at each other state
zero pronoun resolution in japanese discourse based on centering theory
in order to allow ostia to make natural generalizations in its rules we added a decision tree to each state of the machine describing the behavior of that state
thus the branches of the decision tree are labeled with phonetic feature values of the arc s input symbol and the leaves of the tree correspond to the different behaviors
the result of the first merging operation on the transducer of figure NUM is shown in figure NUM and the end result of the ostia alogrithm in shown in figure NUM
to model long distance rules such as vowel harmony in a simple decision tree approach one must add more distant phonemes to the features used to learn the decision tree
in this transducer all arcs leaving state NUM correctly lead to the flapping state on stressed vowels except for those stressed vowels which happen not to have occurred in the training set
this is possible because no samples containing two stressed vowels in a row or separated by an r immediately followed by a flap were in the training data
tences based on tile centering theory
although ostia is capable of learning arbitrary s f s t s in the limit large dictionaries of actual english pronunciations did not give enough samples to correctly induce phonological rules
any sort of simplest hypothesis criterion applied to a system of rewrite rules would prefer a rule such as v v which is the equivalent of the transducer learned from the training data
this section generalizes the previous definition of bilingual class class association score and introduces the bilingual class frame association score
when we used the average of pr ej i vj p ce and pr e vj p cj instead of pr ee cj vj p in the experiment d section NUM most discovered clusters consisted of only one example
the reason is that parallel sentences are useful for resolving both syntactic and lexical ambiguities in the monolingual sentences
in this method translated english verbs and case labels are used to classify senses of japanese polysemous verbs
then among those pairs of c and f j search for a pair eel and fji which gives maximum association score max a ce fa iv j a and collect the ele ce fj ments of eg which satisfy the restrictions of cei and fji into the set eg va eel f i
oak will be discovered for intransitive senses while pairs with the object case like cek l obj cdk q cek obj csk will be discovered for transitive senses
among the elements e of eg va let eg va ce be the set of those whose semantic label seme of the english predicate satisfies the class ce i.e. seme ce and eg vj fd be the set of those which satisfy the japanese case class frame fa i.e. e f fj
of course all these factors can be interrelated
while our prototype system has used only spoken and text english and graphical pointing highlighting or arrows the approach could conceivably involve full graphical capabilities mechanical devices or other inputoutput media
t hc question arises of whether these t heories can account rot sol s
NUM NUM se oild clirrei1co exi ressiolls
the hou analysis under the itou approach 3b is analyzed as lbllows
qb illustrate the workings of onr approach we now run through a simple example
first the meaning of onlyl read the letters that sul se sent to pa ul1 is derived
third it is counterintuitive in that it handles separately two classes of data i.e.
the source and target rules are called cfg skeleton of the pattern
we also found some mistakes by lex2
in order to disambiguate word part of speech with a small training corpus genotypes turn out to be much easier to model than the words themselves
in order to increase the accuracy of part of speech disambiguation we need to give priority to trigrams over bigrams and to bigrams over unigrams
first the number of genotypes on which the estimates are made is much smaller than the number of words on which to compute estimates
surface oriented approaches rely on selectional restrictions of
b some physicians have a nurse
but it should indicate the lack in expressive power at least inasmuch as it is possible without a more general formal proof which we can not give here for lack of space
that the sister reading is not excluded for laa is then explicable by the fact that there is no consistent subset of clauses of 14a which is inconsistent with mp
what is consistently said in the inconsistent discourse does not violate tile mfor the sake of simplicity we were confined to short and simple examples and could therefore not avoid stone artificiality
in the second step we separate out those proon which are in fact disamt iguating and illustrate in the last step tidal the discourse structure imposes further restrictions on the accessibility of premises
null to illustrate which kind of iimoinpleteness we need we assume that the meaning postulates and the discourse can be e xt ressed in a first order language without flmction symbols and identity
although it is possible to derive from the semantic representations of 13a c a contradiction these proofs are by no means disambiguating inferences since the meaning postulates are not involved
since each single clause of such a representation must be satisfiable we can identify the set of consistent information pieces provided by a discourse with the set of clauses of the discourse in scf
not only do our parse trees contain semantic annotations roles and more syntactic detail we also rely on the more informative parse action sequence
nevertheless we suggest doing a human check only on important tests such as final evaluations
this leaves a much smaller amount of work especially if there are many exact matches
the number of crossing errors ce is NUM both in the second sentence
we suggest perforlning two types of tests regular tests and tests with a human check
ny number of brackets in the treebank that were reproduced by b but not by a
yn number of brackets in the treebank that were reproduced by a but not by b
having two variations of the same parser we were interested in the difference between them
nn number of brackets in the treebank that were not reproduced by both a and b
kameworks has not fully c wflual ed with nat urally oc urring
antecedent of a zero pronoun is searched from tile previous sentence to the previous ten sentences
for exmnple consider the following sentences a taro wa issyoukenmei benkyou siteita
i table NUM the performmme of the systems base NUM on walker s algorithm
figure NUM the blocked lcg analysis of the ungrammatical NUM
p our lcg account analyses these constructions in a similar way
given a derived fact or seed magic cat l property l bottom up evaluation of the abstract grammar in figure NUM leads to spurious ambiguity
book with the magic compiled grammar from figure NUM right hand sides of the rules in the grammar as discussed in section NUM NUM
it is however possible to eliminate the subsumption check through fine tuning the magic predicates derived for a particular grammar in an off line fashion
the inference is too powerful in explaining a speaker s intention and the propositional content of the utterance by one key word or phrase
thus the outcome of the rule applications is independent of the order of rule applications
such transducers ignore and pass through unchanged parses that they are not sensitive to
furthermore it is also possible to use rules with negative votes to disallow impossible cases
form while another is form derived by a productive derivation as in NUM and NUM below
null for every token receiving the highest tokens are selected
the goal is to have both recall and precision as high as possible
m NUM selects the highest scoring parse s
our approach is quite general and is applicable to any language
NUM sort all remaining likelihood estimates l u v from highest to lowest
therefore we looked instead for forms ending in ed and ing which had been tagged as verbs
there are a variety of well known techniques for smoothing probability distributions which avoid assigning zero probability to unseen events
a more sophisticated system for acquisition of accurate frequencies for each word would have to be capable of sense disambiguation
we also show frequencies of the er nominal which we assume is derived from the verb form
but it is also demonstrably inadequate at least for systems which are not limited to a narrow domain
however there might also be phonological effects since many dance names are taken from languages other than english
control of syntactic ambiguity by the use of lexical and other probabilities has been demonstrated by several authors e.g.
this can be achieved by estimating the held back probability mass to be distributed between the unseen entries using the basic smoothing method and then distributing this mass differentially by multiplying the total mass for unseen entries expressed as a ratio of the total observations for a given word by a different ratio for each lexical rule
for quasi correct sentences parsing must be performed for all r rmax while for sentences with syntss it must be done only for r NUM if a correct sentence is to be checked or for r k if k distortions are to be corrected
decrement the old rule probability by the same amount
scale the remaining production probabilities to sum to unity
the speedups obtained with this technique can be substantial
the completion pass can now be implemented as follows
to this end each agent disposes of a set of procedures that execute the actions
addition indicates multiple derivations of the same state
the contribution of such a production to the left corner
and the corresponding lower are replaced by their kleene star flmction
the summations for all completed states turn out as
a right bracket i marks the beginning of a complete right context
consequently they are represented by networks thai contain no fst pairs
the bidirectional replacement is unambiguous in both directions
NUM NUM rei la em ent of the empty string
we distinguish four ways how context can constrain the replacement
the replacement maps those brackets without looking at the actual contexts
operatot describe regular relations rather than regular hmguages
the processor identifies the transitivity features associated with each matching clause and produces a ranked output of documents based on the weights assigned to each clause in which the search terms occur
tionships and the a ccura y ff NUM ositil ning of the node
howew r ill trla lly cases these strong similarities are caused by less typica l
as shown in figure NUM NUM NUM relationshil s are needed to est im te the nodes
the viewpoint of a node in the the sa urus is estimated by using a certa in
for example suppose the word sentouki fighter NUM is not contained in a thesaurus
to overcome the prohlem of data sparseness distinguishing features of each node called viewpoints are
relati mshil s ofc m imcl ed odes in the NUM hesa urus
an indexed grammar is a cfg in which stack of symbols are associated with non terminals
we can easily check that the other form of productions have a lesser degree
each one corresponding to a different cutting out of x wcw i.e.
this previous paper describes a recognition algorithm for ligs but not a parser
the class of mildly context sensitive languages can be described by several equivalent grammar types
we now look at the ldg complexity when the input lig is a liged forest
we can observe that this shared parse forest denotes in fact three different parse trees
to insert the semantic part of the tag xanthippe presents a panel representing all possible semantic continuations of the syntactic part of the tag selected
ctal NUM but differs tuorc l roln the ibm h atimlar than our l agset does t roln the laws tagsel
moreover a very large highly del ailed part of speech tagset is used to label each word of each sentence with its syntactic a d semantic categories
by collt rast price fell is unobjectionable in l igure 3a rendering that parse more probable
alter presenting a novel o n a parsing algorithm for dependency grammar we develop three contrasting ways to stochasticize it
the link to i from its eth child is associated with the probability pr in NUM
the pilot experiment was conducted on a subset of NUM of the sentences comprising NUM a NUM words and punctuation marks
unfortunately any information about discourse structure is absent in semcor apart from sentence endings thc performance would gain from the fact lhat sentences from unrelated topics wouht not be considered in the disamhiguation window
this could account for the fact that in br r05 composed mainly by short pieces of dialogues the best results are for window size NUM the average size of this dialogue pieces
meronymy does not improve performance as expected
partial disambiguation is treated as failure to disambiguate
table NUM shows some statistics for each text
figure NUM algori hm for each window
non noun words are lot taken into account
one crucial insight into the working of the algorithm is that although both prediction and completion feed themselves there are only a finite number of states that can possibly be produced
we will not try to give a precise characterization in the case of sparse grammars appendix b NUM gives some hints on how to implement the algorithm efficiently for such grammars
outer probabilities complement inner probabilities in that they refer precisely to those parts of complete paths generating x not covered by the corresponding inner probability 7i kx a
a state with the dot to the right of the entire rhs is called a complete state since it indicates that the left hand side lhs nonterminal has been fully expanded
the same threshold is used to simplify a number of other technical problems e.g. left corner probabilities are computed by iterated prediction until the resulting changes in probabilities are smaller than e
in fact the definition makes no reference to the first part a of the rhs all states sharing the same k x and will have identical outer probabilities
the particular way in which NUM and fl were defined turns out to be convenient here as no reference to the production probabilities themselves needs to be made in the computation
thus linking ceases to be a mere test or filter and can instead function as an independent device for the transmission of information in parsing
we quote shieber s syntax rules for his second analysis of subcategorization p NUM in which the subject of a verb appears as the first element of the subcategorization list
in the next section next q i is used to select the index of the rule that should be next applied at node n after the first i NUM rules of r have been considered
in this way the decision of the dta is affected not only by the portion of the tree below the currently read node but also by each subtree rooted in a left sibling of the current node
every time a node is read the current state of the device is computed on the basis of the states reached upon reading the immediate left sibling and the rightmost child of the current node if any
it is not difficult to see that l m l NUM NUM observe that when we restrict to monadic trees that is trees whose nodes have degree not greater than one the above definitions correspond to the well known formalisms of deterministic finite state automata the associated extended transition function and the regular languages
we call p chain any sequence of one or more subtrees of c all matched by q that partially overlap in c let n be a node of c and let q state n
the remaining four could arguably be classified as commerce finance which was identified as the second most similar domain to email
in contrast to the separation along linguistic levels this approach adopts a functional view cutting across linguistic strata
since the sem parser does not directly work on linguistic input there are two possible parsing modes non autonomous parsing
this makes it possible to restrict the conmmnication between the parsers to intbrmation about what rules were successfiflly or nnsuccessfiflly applied
if a parser treats all constraints on a par it can not distinguish between the structure building and the filtering constraints
the cost s of these operations in time and space increase exponentially with the size of the structures
we abbreviate these subgrammars by g v and g and tile original grammar by g
analogous to partial evaluation of definite clauses we can partially evaluate annotated grammar rules since they drive the derivation
having passive status is a necessary but not sufficient condition for an edge to be sent as hypothesis
another consideration to be taken into account is that the analysis should be incremental and time synchronous
clearly if many disjun tions are involved the speedut might even be exponential
consequently a lm derived from a heterogeneous corpus should have a higher perplexity than an equivalent one derived from a more homogenous corpus
in the middle are domains such as world affairs social sciences commerce finance that correlate with both poles to varying degrees
it is commonly applied in speech recognition and part of speech tagging for adjusting the frequencies of un seen word sequences e.g.
secondly recursivc rules couhl introduce infinitely nlany solutions for a given utterance
the cost of such tests and the on line type expansions need further investigation
this set of information is what we call a subgrarnraar
the subgrammars represent the distribution of information across the parsers
semna rot resents results for tile sem p u ser
NUM NUM NUM tile interface is shown in figure NUM
this task is performed by the drafting tool which is briefly described in tile next section
more detail on the drafting process can be found elsewhere
these are presented to the author by tile draft text viewer
examples of endexpr bigrams table 2b
NUM NUM utterance model with local co
for example the input vc receives the description NUM shown in example NUM
computing them is just like computing other cell entries except that only overparsing operations are considered
the number of loop iterations is the number of passes through the overparsing operations for the block
error correcting parsing uses optimization only with respect to the faithfulness of pre defined grammatical structures to the input
it does not define the set of grammatical structures it defines the space of candidate structures
the set of possible position structures is defined by a formal grammar the position structure grammar
the dp table is perhaps best envisioned as a set of layers one for each category
a layer is a set of all cells in the table indexed by a particular cell category
there are three main types of operations corresponding to underparsing parsing and overparsing actions
it is possible to reduce this value still further but only by compromising the overall recall value i.e.
the logical next step is therefore to compare word bigrams or trigrarns instead of just unigrarn data
moreover it has the advantage doing so by considering typically uuigram bigrarn and trigram data
email is by far the most heterogeneous more so even than the unclassified section of the bnc this brings into question the results of the similarity calculations in which the email corpus was involved and mitigates further against the strategy of augmenting the email corpus with texts selected using the top down
to use perplexity both as a similarity metric and an evaluation metric implies a certain amount of circular reasoning
generic speech recognition systems typically use language models that are trained to cope with a broad variety of input
these characteristics are defined using a number of statistical measures and their significance for language modelling is discussed
a number of threshoids were investigated and the optimum value determined empirically was around NUM NUM words
for each domain corpus do times NUM ii NUM NUM divide the corpus into two halves by randomly placing 5k word chunks in one of two subco o l NUM NUM produce a word frequency list wfl for each subcorpus i NUM NUM calculate the rank correlation between the two subcorpora ill NUM calculate the mean and standard deviation of r
it fonows that multiple correct parses exist for many sentences since by det nition any change in tag means a change in parse
in the machine interpreted setting the agent used a significantly higher percentage of words first p NUM the client accommodated to the agent
recall that clients in the machine interpreted setting unlike those in the human interpreted setting did not use words in common more than the agent did in subsequent conversation
one way is to use the resources of a multimedia environment to replicate the effect of the human interpreted setting by providing the machine interface with a human like persona
we have suggested that the design of speech recognition and language processing systems can take advantage of users lexical accommodation to machine interfaces to improve system performance
given this possibility then we predict that the results will show client accommodation to the machine interpreter but at a lower level than in the human interpreted setting
of course there is no a priori reason why interactors could not conduct conversations in completely different styles using different phonologies sentence structures vocabulary etc
this type of accommodation is one way in which users adapt to the limitations of computer interfaces i.e. they converge to the limited lexicon of the computer
for real time real situation human computer interaction to approach reality the burden of understanding and conveying information can not be shared equally between the two interaetors
humans need to make allowances for features of the computer interface such as synthesized speech limitations on range of knowledge base and imperfect speech recognition
alternatively lt lcb s may be compiled out bat nnder this approach too problems like directionality and the blocking of ll lcb s as well as expensive ambiguity at parsing time remain unsolved
n quot wi i h i ij v ilo ivp lti pp iiilll load m
NUM gives the fragment of the type system constraining the wdues of the synsemiloc continucleus path in the word description of prepositions which participate to the locative alternation l henomenon
only limited inference power is necessary for this set up to work the system must be able to infer that the unification of a subtype with its supertype is of the type of the subtype
i rocessiug in this system is normally divided into separate structure building and feature decoration rule coati orients however for our l ui poses no use was made of this distinction
in this the situation is precisely the same us with lexieal rules for in each case what is provided is simply a compact representation of an ambiguity
we will first show that their role is less clear than this suggests and certainly more problematic before suggesting in section NUM an alternative which eschews any extra mechanisms
the parsing of a trivalent version as before would involve backtracking on the rule dealing with optional complements but then the rule dealing with obligatory ones would be chosen and it would succeed anyway
this approach can be adopted whenever inforrua tion
in this section we will show the application of constituent boundary patterns based on the concept of bottom up chart parsing
we will then explain how constituent boundary patterns can be used to describe the structure of an input string in tdmt
from the combined structure for NUM the sentence below is generated after adjustment necessary for japanese grammar
NUM could i please have your name the date of arrival and the number of persons in your party
an efficient application of transfer knowledge source parts to an input string plays a key role in achieving quick translation
the technique for obtaining substructure preference is the determination of the best substructure when a relative passive arc is created
also many arcs can be chained via non terminal symbols such as a part of speech and np noun phrase
in a top down and breadth first application all the possible structures are retained until the whole input string is parsed
the content words bus goes chinatown and ten create passive arcs
therefore our new method can efficiently translate a longer input string having many ompeting structures
NUM smith spent in in his paycheck
the variable p4 represents the t roi erty of wanting ul to po
examine a simph case of sloppy iden lily in vl ellipsis NUM rcb l
the proposed theory permits a simple llniforln treatment of sloppy identity in vpe and paycheck pronouns
lasher NUM examines a variety of extensions to the i r
there is an important difference in the way discourses are viewed in centering and in dynamic semantics
we define two types f transitions fi om he ul
centering theory claims that a discourse always has a single topic or center
c ve also evaluated the method by conducting pp attachment disambiguation experiments using an automaticmly constructed thesaurus
we address the probhml of automaticmly constructing a thesaurus by clustering words based on corpus data
c NUM is the nnnlber of free parauleters ill tlle nlodel
our hest disaml iguatioll rcsull obtained using t his last combined niet tiod
t is mways non negative a nd is zero iff the two distributions arc identical
we employ the simulated a m ealing technique to deal with this problem
our experimental results indicate that such a thesaurus can be used to improve accuracy in disambiguation
in NUM and NUM above the numbers NUM NUM NUM and NUM correspond to the roles agent ag theme th predicate pred and possessional modifier mod poss respectively
for example the following primitive representations are produced for the spanish word a at loc to loc at loc to poss at poss toward loc at lot
consider what happens in a lesson if the author has specified that a correct answer to the question addnde paso jack el libro in spanish is jack fir6 el libro a la basura jack threw out the book into the trash
one important benefit of using the levin classification as the basis of our program is that once the mapping between verb classes and lcs representations has been established we can acquire the lcs representation for a new verb i.e. one not in levin simply by associating it
and the constant decorated replaces the wildcard NUM output be ident thing NUM at ident thing NUM decorated NUM with poss head thing NUM ii
these numbers enter into the construction of lcs entries they correspond to argument positions in the lcs template extracted using the class grid lexeme specification hfformatiou is filled into the lcs template using these numbers coupled with the thematic grid tag for the particular word being defined
in order to inform the student whether a question has been answered jack threw the book in the trash jack threw the book in the trash exact match that s right jack put the book in the trash jack threw the book in the trash missing manner how
a full entry in the dal abase includes a semantic class number with a list of possible verbs a thematic grid and a lcs template NUM class NUM NUM adjoin intersect meet touch thematic grid th loc lcs template be loc thing NUM at loc thing NUM thing NUM
we had available is insufficient its coverage is smaller than that of a hand made thesaurus
if this japanese form is translated literally into english the result would be ca n t you do x for me which has a quite different pragmatic meaning and definitely does not convey the same degree of politeness as the japanese expression
we base our methodology on the fact that such antecedents are likely to occur in embedded sentences
they refers to alfred and zohar af the focusing algorithm confirms the cf
there are other classes of verbs such as want to hope that and so on
NUM john claimed that the picture of him hanging in the post office was a fraud
this means applying the step NUM of the algorithm to ee2 then step NUM to eel
the expected focus is generally chosen on the basis of the verb semantic categories
starting with these considerations the algorithm is governed by the hypotheses expanded below
these constraints are used to determine relations between a given anaphor and its antecedents
this selection may be confirmed or rejected in subsequent sentences
a can also be estimated empirically
co occur is called a direct association
we propose that the spoken language phenomena that have been labeled as disfluencies or ill formedness be divided into two categories those that serve a communicative function and those that are non communicative by products of the speech production process
on the other hand a verb such as grew which selects solely ap complements 3a requires that its complement satisfies noun verb
in the lcg account presented above the agreement failure in him runs is reflected by the failure of acc to imply nora not by the inconsistency of the features acc and nora
asymmetries interestingly the analysis of coordination is the one place where most unification based accounts abandon the symmetric consistency based treatment of agreement and adopt an asymmetric subsumption based account
children dat however the lcg analysis systematically distinguishes between frauen which is assigned to the category npaaccadat and mdnner und kindern which is assigned to the weaker category np accvdat
since only a finite number of feature distinctions need to be made in all the cases of agreement we know of we posit only a very simple feature system here
since strengthening the antecedent of an implication weakens the implication as a whole the combined effect of rule p and the introduction and elimination rules is to permit the overall weakening of a category
now this account presupposes the existence of appropriate underspecified categories e.g. in the english example above it was crucial that major category labels were decomposed into the features noun and verb
using the lcg account described above it is necessary to treat adverbs as ambiguous assigning them to the categories s np sg s np sg and
in fact if categories such as np and ap are decomposed into the conjunctions of atomic features nouna verb and q noun verb respectively as in the sag et
they are used to reduce the number of features to a manageable size and to exclude words that are expected to be given unreliable similarity values
as long as the frequencies are not very high it does not label rcb yl whose frequency is twice that of w2 as less informative
figure NUM compares the similarities of a narcotic example to the narcotic sense and to the medicine sense for each iteration
the number of examples which resulted is shown in row NUM of table NUM labeled final coding
the parse accuracy is defined as the percentage of test sentences for which the most probable parse exactly matches with the test set parse
in previous work dop was tested on a cleaned up set of analyzed part of speech strings from the penn treebank achieving excellent test results
output is a flflly specified feature description subsumed by the input struel ure which is then linearized to yiehl a sentence
their technical discussions and suggestions greatly helped me shape the idea of pattern based cfgs
therefore only the head constraint violation in the target part is accounted for in our prototype
more specifically if the former is larger than the latter we attach it
that of one based on the maximum likelihood estimator mle for short
we used as training data the same NUM NUM case fl ames in experiment NUM
this problem is usually referred to as the data sparseness problem
the obtained thesaurus seems to agree with human intuition to settle degr e
thus the coarseness or fineness of clustering also determines the degree of smoothing
the symbol v in the target rule must have the verb manquer as a syntactic head
this research would have not been possible without the help of graeme hirst there are no fight words to thank him for it
furthermore we use only surface based methods for determining the markers and textual units and use clauses as the minimal units of the discourse trees
i g at tl eq d i but water icewal r andclouds blowing du orcarbon dioxide
assume now that we can infer that although marks a concessive relation between satellite NUM and nucleus either NUM or NUM and the colon
this information is used by an algorithm that concurrently identifies discourse usages of cue phrases and determines the clauses that a text is made of
unlike the information in a corpus the information in the mp d definitions is presorted into senses
however as noted above the mrd definitions alone do not contain enough information to allow reliable disambiguation
third in addition to the examples of the polysemous word w in the corpus we learn also from the examples of all the words in the dictionary definition of w in our experiments this resulted in a training set that could be up to NUM times larger than the set of original examples
a appendix a NUM stopping conditions of the iterative algorithm let fi be the increase in the similarity value in iteration i fi x y simi x y simi l y y NUM where x y can be either words or sentences
this does not obviate the need for manual work as producing bilingual corpora requires manual translation work
words are considered similar if they appear in similar contexts contexts are similar if they contain similar words
we expect that a similar improvement could be achieved if that constraint were used in conjunction with our method
the problem remains however that the word translations do not necessarily overlap with the desired sense distinctions
the total weight of a word is the product of the above factors each normalized by the sum of factor w factors of the words in the sentence weight wi s w esf ctor wj s where factor is the weight before normalization
that the per sentence parsing acclzracy suffers when parses are predicted from raw text
this yields lower bounds on potential accuracy at low cost
this is the solution that was adopted in creating the atr lancaster english treebank
suggests any candidates which are as likely as the correct un wer
for ibm manua s task see table NUM above
moreover integers and strings are considered to be subtypes of the types string and int respectively and are also treated as constants
the semantics of an utterance is given by a set of possibly partially specified feature structures that are stored in a discourse history
top level frames can be changed during dialogue interaction to check if the input is or is not conform to what has been expected
in current natural language processing systems different components for different processing tasks and input output modalities have to be integrated
the construction rules operate mostly on the semantic slots in the parse trees so that synonymy and paraphrases of expressions can be handled
the partial semantic parse trees are converted to a semantic representation by traversing the parse tree and applying construction rules to the nodes
however at the time being there is no possibility to determine if the input corresponds to the language model or not
once this is achieved the index of the intersection of the destination is stored in the path object by the following rule
the paths and their values are mapped to strings that are filled into a template to produce the questions shown in b
in order to adapt the rule one would only have to replace lcb obj path rcb NUM dst with lcb speechact orderobject rcb
thus washington is likely to co occur with either president washington or washington d c but not with both
more commonly however several pieces of positive and negative evidence are accumulated in order to make this judgement
without knowledge of the official name it is sometimes difficult to determine the exact boundaries of a proper name
if it is found only in sentence initial position e.g. white paint is white is discarded
thus in the absence of any other evidence in the document beverly hills is classified as a person
instead nominator makes use of a different kind of contextual information proper names cooccuring in the document
this high percentage is due to a decision not to assign a type if the confidence measure is too low
as mentioned before during aggregation linked groups from different documents are merged if their canonical forms are identical
some combinations may result in a high negative score highly confident that this can not be a person name
other advantages of using limited resources are robustness and execution speed which are important in processing large amounts of text
the base lexical entry is fed into the first argument of the call to the interaction predicate q l
otherwise there would be no information available to restrict the search space of a generation or parsing process
the in specification of a lexical rule specifies the in feature the out specification the derived word itself
NUM NUM note that the passivization lexical rule in figure NUM is only intended to illustrate the mechanism
second it provides a way to automatically obtain a definite clause encoding of lexical rules and their interaction
applying constraint propagation to the extended lexical entry of figure NUM yields the result shown in figure NUM
with respect to our test grammar the constraint propagated covariation lexicon thus is the fastest lexical encoding
special care has to be taken in case the same lexical rule can apply several times in a sequence
NUM for the computation of the follow relationships the specifications of the frame predicates are taken into account
the table may also be filled in a more left to right manner bottom up in the spirit of cky
thus a block of cells the set of cells each covering the same input substring is interdependent with respect to overparsing operations meaning that an overparsing operation trying to fill one cell in the block is adding structure to a partial description from a different cell in the same block
example NUM the optimal description for vc s f y m c p v m c the surface string for this description is cvc the first c was epenthesized to balance with the one following the peak v
one exception would then have to be made to permit input segments prior to any parsed input segments to be underparsed i.e. if the first input segment is underparsed it has to be attached to the left side of some constituent because it is to the left of everything in the description
this paper describes an algorithm for computing optimal structural descriptions for optimality theory grammars with context free position structures
each block of cells for an input subsequence is processed in time linear in the length of the subsequence
otherwise the cells would be empty and the overparsing operations would have nothing to add on to
a partial description is much like an edge in chart parsing covering a contiguous substring of the input
the special case of strict locality is easy to understand with respect to context free structures because it states that the only information needed about a subtree to relate it to the rest of the tree is the identity of the root non terminal so that the necessarily finite set of non terminals provides the relevant set of classes
when r has just received ml i NUM NUM she does not know whether the game has been played through tl or t2
this preference is accounted for by the preference for parallelism concerning the combination of semantic content and grammatical function in both ul and u
a strategy of each player in such a compound game associated with a compound expression is a combination of her strategies for all such constituent games
in order to test these ideas we are currently converting modules in our existing system to allow experimentation with various communication languages and architectures
a more complex example of default inference is provided by right as a modifier of side
this dependence is highlighted most clearly by comparing the performance of john s justeson and slava m
only NUM do have so many minority instances covering NUM of the NUM total occurrences
computational linguistics volume NUM number NUM the converse result obtains in the case of house
we refer to these as cases of a predicative indicator feature
we refer to these as cases of an infinitival indicator feature
the adjective light when modifying a concrete noun is a case in point
hoover leaped from his car and ran to the left side of the gangster s car
the sentences of this subcorpus contain NUM such co occurrences of the target adjectives and their antonyms
in addition we stripped morphological suffixes from noun phrases to recover an adjective noun base
NUM total number of sentence express ons being not re
figure NUM summary examples using the properties f0 the text classification
many subjects still need to be tested NUM using of the thesaurus most fmlures in processing news articles were caused by synonyms such as corpse and dead body fishery and fisherman to be matched most of these errors can be corrected by using the thesaurus
system consists of a goal detection and sentence selectmn process by informativeness evaluation the goal directed method may be sound overstated because the current experimental system handles only the headhnes htles and some text property expressions however the goal directed rcb method is named as the first step toward real zing rcb a context based summarmatmn system figure NUM
its expressivity since we state the problem in terms of constraints between labels
in this section we will describe the experiments performed on applying this technique to our partieular problem
relaxation labeling is an optimization technique used in many fields to solve constraint satisfael ion problems
the number in parenthesis is the iteration at which the algorithm should be stopped
the interest of this corpus is to test the algorithm with a large tag set
its flexibility we do n t have to check absolute coherence of constraints
null study back off techniques that take into account all classes and degrees of constraints
grammar learning from corl ora can be done with concise and linguistically well defined ore grantitt tr
f the execution is terminated c must be unifiable with goals
the core structures arid the dependency analyzed dfss that augment the la are shown in figure NUM
this results in rather poor tagging accuracy so it is quite possible that a manually tagged corpus would produce better results
in all the most accurate technique achieved an accuracy of NUM as compared to the NUM achieved by guessing left branching
in this study conceptual association is used with groups consisting of all categories from the NUM version of roget s thesaurus
given the high level descriptions in section NUM NUM it remains only to formalise the decision process used to analyze a noun compound
some cases the sequence was not a noun compound nouns can appear adjacent to one another across various constituent boundaries and was marked as an error
by assuming that all words within a group behave similarly the parameter space can be built in terms of the groups rather than in terms of the words
not only does this require a vast amount of memory space it creates a severe data sparseness problem since we require at least some data about each parameter
if the ratio is exactly unity the analyser guesses left branching although this is fairly rare for conceptual association as shown by the experimental results below
if the same system were to be applied across all of af a total of NUM NUM nouns then around NUM NUM billion parameters would be required
if lb push es this button then c b can go oul native speakers of japanese have the following intuitive interpretation for NUM without any special context
on the other hand in the case of the sentences NUM and NUM the subjects of the matrix clauses can be either users or ma hines
consequently the subject should be neither the speaker nor the hearer due to the constraint that we can not express sortie volition o1 request in a matrix clause of the to sentence
as the first step to confirm this expectation let us examine whether the matrix clause may have a request form or not in the cruses of reba tara and nara
as described in fable NUM in those eases the subordinate clause shows an assumption rather than a cause and the matrix clause may be a request as shown in the following example
as we exepcted it shows that the matrix clause of the sentence with tara or nara may have a request form that is the subject of the matrix clause may be a user
the accuracy of the default rules of i o reba tara and nara is NUM NUM NUM NUM NUM and NUM respectively as far as we examined
as described above there are many cases that linguistic expressions give us a key information to resolve some tyl e of ambiguity like the a nal hora of a zero pronoun
they are important portions of our future work
NUM NUM default rules of usage of reba tara
as a result of this we obtain both noun clusters and verb clusters
context we derive constraints on interpersonal meaning which are then expressed through intonation contour or tone contour or simply tone
as a default the system 4tone moreover realizes the logical metafunction however we will ignore this fact for the present argument
as illustrated in section NUM NUM the relation between dialogue moves and tone is many to many hence the appropriate tone selection must be further constrained
9we assume here that the information unit is the clause and that tonality is unmarked i.e. that there is one tonegroup only
one of the primary grammatical choices relevant for the selection of tone is the choice of mood such as declarative interrogative and imperative
a pass or passes since negotiation is recursive through the network results in a syntagmatic structure of an interaction called exchange structure
in this section we discuss our proposal of bridging the gap between the dialogue model and the text generator komet penman from a top down perspective
this is relevant for geographical clarification question e.g. wollen sie nach frankfurt am main oder frankfurt an der oder
for instance the genre of information seeking human machine dialogues is characterized by certain genre specific stages or dialogue moves see section NUM NUM
such an organization is for instance proposed in systemic functional work on interaction and dialogue NUM NUM NUM
this would be an interesting empirical confirmation of the need ibr non linear obliqueness
a maria thlou acerca do pedro consigo pr6prioi
temporal forgetting at first the input activities are slightly modulated by a process of temporal forgetting
relative completeness any argument might remain unfulfilled although the parser must always favor the more complete analysis
one such topic is the subcat fbature and the inibrmation encoded in it
as mentioned above the microsemantic parser ignores in a large extent most of the constraints of linear precedence
in this paper we argued the structural variability of spontaneous speech prevents its parsing by standard syntactic analyzers
to overpass this problem we propose in this paper to handle the spoken language without considering syntax
now the extreme structural variability of the spoken language balks seriously the attainment of such an objective
every recognized word is finally handled by the parsing process with its priming relation see section NUM
unification the microsemantic parsing relies on the unification of the subcategorization frames of the lexemes that are progressively recognized
provide same formulation of the same question or addre to users every where in the s rcb steln s dialo uc turns
rior to the user test section NUM we colni ared the principles with h ice s cooperative principle and maxims
the NUM dialogues that were recorded during the last two woz iterations were performed by external subjects whereas only system designers and colleagues had participated in the earlier iterations
the principles discussed in this section appear irreducible to maxims and thus serve to augment the scope of a theory of cooperativity
sunlmarising the generic principles NUM NUM may he replaced by maxims gpi gp2 and gp5 gp9
we perlormed seven woz iterations yielding a transcribed corpus of NUM task oriented human machine dialogues corresponding to approximately seven hours of spoken dialogue
section NUM briefly describes how the principles were tested the on user test dialogue corpus and section NUM concludes the paper
the machine is not a nornml dialogue partner and users have to be aware of this if communicalion faihire is to be avoided
this reduces the problem size from tens of messages into much smaller sizes
null NUM identity deletion deletion of identical components across messages
puts the messages that have the same attribute right next to each other
based on the ranking the system reorders the messages by sorting which
should not generate sentences that are too complex or ambiguous for readers
aggregating all three messages together will results in questionable output
NUM are ignored because they are always the same at this stage
d is now defined as the m tuple of dependencies n lcb af NUM af NUM af m rcb
it is included in the model by defining an extra distance variable a and extending c f and to include this variable
the prepositions main candidates for attachment would appear to be the previous verb rose and the basenp heads between each preposition and this verb
we think that a proper implementation of deleted interpolation is likely to improve results although basing estimates on co occurrence counts alone has the advantage of reduced training times
people find that punctuation is extremely useful for identifying phrase structure and the parser described here also relies on it heavily
there is no grammar as such although in practice any dependency with a triple of non terminals which has not been seen in training data will get zero probability
on receiving this set of interpretations the integrator can not immediately execute the complete interpretation to create a fortified line even if it is assigned the highest probability by the recognizer since speech contradicting this may immediately follow
figure NUM applying the pplr to kdnnen under unification
NUM das auto wurde kaufen gekonnt
left hand side of the lexica rule
match one of the human produced parses
in what follows section NUM explains the contribution to the prediction process of the grammar and of the lexical generalizations created by our grammarian
formulating grsmmar and lexical questions for prediction we have developed a flexible language for formulating grammar based and lexically based questions about treeb n text
the facts corresponding to lexical entries are ignored
unfolding can also be used to collapse filtering steps
briscoe et al NUM but these suffer from the disadvantage that detailed lexical semantic information must be available to detect potential synonyms
the causes of these errors are being evaluated
unfolding can be used to eliminate superfluous filtering steps
b incorrect antecedent but correct verb NUM cases
additional approaches to the problem of subdeletion were suggested
potential problem situations for the algorithm were also presented
NUM improving performance in the case of
an algorithm for solving the error category of subdeletion was described and examined
for the automatic summarising experiment all sentences within a text will be coded
other transformations are based on clustering either expert or automatic
note that since the result of a combination always bears an index set larger than either of its parent formulae and since the maximal index set that any fornmla c n carry includes all and only the indices assigned to the original left hand side formulae the above process nmst terminate
otherwise it asks the user what to do with the match that was found
subsequent tagging and morphological parsing then skip these tags and further processing i.e.
this version with usr tags inserted is then processed by the set of lift rules
the program stores it as a non proper noun and proceeds
the tit component of the alep platform also foresees the integration of user defined tags
general format level and sentence level phenomena can be handled in a similar way
but both of them amount and currency are defined as being optional
partial linguistic structure in alep terminology this conversion is called lifting
syntactic analysis is based on the tag value not the original input string
the regular expressions can be stored in variables and reused to build more complex expressions
whether predicates in our case the attribute can or are determined before the argument they qualify here man remains an empirical question
then 2degactrua ly rules names with respect to the atr engksh grammar d NUM NUM v
much more crucial for disambiguation are the restrictions imposed on the assertional language
some of these sisters admire stars who got an oscar
mccord NUM sometimes supplied by an external type hierarchy ontology e.g.
we could rule out the mmesired reading given in NUM
b some physicians tried to marry their sis null ters
NUM a some physicians haw a sister
NUM some physicians have a sister to whom they are married
disambiguation to limit inference is a well known strategy employed for knowledge retrieval e.g.
NUM NUM phys zn lcb x
the rest of the rule patterns were eliminated because they represented idiosyncratic bracketings and category assignments in the original corpus and so were covered by other rules
the colon expansion should not be bracketed as an adjunct to the ve but rather as an adjunct to the whole sentence in order to make linguistic sense
it should be noted however that whilst all the twelve patterns in table NUM are valid not all of them are normal colon expansions
to introduce conjunctive lists where the verb subcategorises for sentences or noun phrases and also in certain writing styles to introduce direct speech NUM
the only t roblem is that it is not necessarily suitable for all i he resulting structm cs to NUM e referred to as sentences
it can process any context free rule format without conversion to some normal form and combines computations for a through d in a single algorithm
what is the probability that a given string x is generated by a grammar g what is the single most likely parse or derivation for x
this is mainly expressed through the speaker s choice of conversational topic lexical items and syntactic structures
figure NUM reports some lexical entries
argument haines ill a secolld slep we encode the resulting or dered mary free inh a himuy forumt which makes explicit the order in which dependents are incorlmrated inlo their head
ornis however is tilat they do not have unainbiguous readhigs in cases where the rehliive scopes of constituents can result in clifl erent semantic ii terpretations
on the other hand the sentence peter hates every woman that john does not like would be assigned tile t form or f i
where there is a conflict between predicate argument directionality and dependency directionality are no tated ill the u form throngh negative labels and correspond to llodifie
binding of variables is done at tire time of incorporation permitting tnttch lexibility in composition order and a simple account of the semantic effects of permuting several incorporations
in order to nlake it oi lllal we need to encode our representation into a binary tree fornmt ell which a compositioiml senlan tics can he delined
the litst ftmction is of type e while the second function is oi type e t lor hate hi h2 is of type t and hl of type e
u ferms are nnordered labeled n ary trees such as tile one shown in fig l corresponding to tile the edge htbels me members of the set lcb det NUM NUM NUM l NUM
if we now go back to our example we have to con pose in complement mode the function peter ol type e with the ftmction th i hate h
subclasses of verb may of course override any part of this default for instance transitive verbs add a second syntactic argument for their direct object this example introduces several new techniques
the simplest descriptor is just an atom or variable atoml varl then there are three kinds of local inheritance descriptor a node an evaluable path and a node path pair
however in a datr description specifying explicit values for extensions of the left hand side of such an equality constraint overrides its effect and thus does not influence the values on its right hand side
it might be tempting to conclude from this that the equality operator in datr is very different from the corresponding operator in patr but this would be to misunderstand what has happened in this example
in the following we would like to give some actual figures which may illustrate performance
the grammar is able to parse sentences with up to NUM words in NUM sees
it has to be made sure that the derived relations are as compact as possible
keys keys are values of attributes within linguistic descriptions defined by path declarations
leanness means that computationally expensive formal constructs are sacrificed to gain efficiency
the relation and tension between these parameters are described in this paper
the th component of the alep platform also foresees the integration of user defined tags
a number of points contributing specifi null cally to efficiency should be summarized here
priate choice of head relations is grammar dependent alshtl p NUM
the algorithms will then be spelled out in section NUM
slash ex null traction is handled differently
figure NUM slash extraction slightly simplified
x2morf augmertts standard two lewj morphology in two ways
to be processed rceursively fuf employs two methods
oding this processing st rategy in tim gr tt llila r is given in fig NUM if flmcl ional categories are NUM resent
this makes the lr affected entries not generable fully automatically and this is why each application of an lr to a qualifying phe null nomenon must be checked manually in the process of acquisition
in that case we would recommend testing on more specific structures because otherwise the general result will be misleading
ce crossing error the number of bracket pairs produced by the parsing system that constitute a crossing error against the treebank
bracket accuracy is often lower than it should be when the treebank does not indicate all brackets so called skeleton parsing
we look at the number of equal answers to estimate the number of bracket pairs that were not too easy or too hard
the result of this process is that em ce and sp will be divided in accepted and rejected giving six groups
em exact match the nmnber of bracket pairs produced by the parsing system that are equal to a pair in the treebank
in this article we will show that this makes it impossible to calculate the significance in a straightforward way and suggest two solutions
l he measures in the lower part of this tabh are more directed at the test than at the parsers
we suggest using recall lmrd but when the treebank does not indicate all brackets recall soft may give an indication of the proper recall
thus we require the following modified o el rule where c vr arc
for an overall deduction employing these two formulae to be correct the binding of the two instances of i must be consistent
this observation is of note in that it restricts the form of proofs that we must consider in seeking to prove some possible theorem
l has a product commonly notated as with the lambek implicationals and being its left and right residuals
pie NUM mid proof tic l methods for a range of syst ems roorda NUM
diseii litte l he la bels is x for cxanllile the orma lisnls
such translation shouhl induce lal ellittg hal imi orts the cons fronts of ttlc originm weaker logic
the results for productivity are only strictly comparable within a particular corpus
rereprogram anti anti missile or great great grandfather
however it could be useful with more traditional systems
different applications could utilize probabilistic information in different ways
figure NUM shows part of the corresponding fsm explicitly
figure NUM raw frequencies for some paint nouns
club whip and decorative coatings e.g.
the results are summarized in forms were genuine
in contrast a random procedure recalled on average only NUM NUM of the sentences considered important by the judges with a precision of NUM NUM
figure NUM shows that the region of interest in the parameter space where NUM a a a NUM has only one clearly visible global maximum
we would be able to classify words at deeper levels if we obtained more co occurrence data
this results in the intended utterance which is characterized by natural speech properties such as ellipsis inverted word order or interjections
where the ci are possibly hierarchical feature constraints on a sequence of the morphological parses and v is an integer denoting the vote of the rule
the approach depends on assigning votes to constraints according to their complexity and specificity and then letting constraints cast votes on matching parses of a given lexical item
if any errors are found the outcome of the pruned node s other child is tested
consider again the english flapping rule which applies in the context of a preceding stressed vowel
the cost function for substitutions was equal to the number of features changed between the two phonemes
the costs of edit operations are based on phonetic features we used NUM binary articulatory features
first a dynamic programming method is used to compute a correspondence between input and output phonemes
the algorithm does not have the language bias which would allow it to avoid linguistically unnatural transducers
the algorithm needs knowledge about classes of phonemes to fill in accidental gaps in training data coverage
figure NUM initial tree transducer constructed with alignment information note that output symbols have been pushed
the ostia algorithm can be proven to learn any subsequential relation in the limit
the underlying string of each pair was taken from the phoneme based cmu pronunciation dictionary
since r is now a function of a and a only the latter two variables represent degrees of freedom in the model
since r is constant over all word types it also represents the probability that an arbitrary co occurring pair of word tokens are mutual translations
the competitive linking algorithm implements this heuristic NUM discard all likelihood scores for word types deemed unlikely to be mutual translations i.e.
null so far we have only implemented a two class model to exploit the differences in translation consistency between content words and function words
the test sample contained only about NUM content words NUM and the links for both models were evaluated post hoc by only one evaluator
the cat that the dog that the man bought chased died the man bought the dog that chased the cat that died the man have hans the horses teach feed a NUM utch cross serial dependency shuctuml complexity NUM die maenner haben hans die pferde fuettern gelehrt the men have hans the horses feed teach b
the additions helped in learning more compact accurate and general transducers than the unmodified ostia algorithm
section NUM NUM below shows the result of making these two additions to the method
the drafting rlbol shown on tile far right of the diagram comi rises two major components the text planner and the tactical generator
the authors indicated that they wouhl weleollle tools to hell them collect the apl ropriate information and create a formal representation of the resulting model
the central data structure by which synchronization and communication between tile parsers is achieved is that of a completion history containing a record on how a subtree was completed
since the feature vector has a fixed length unification of two feature vectors is performed in a constant time
NUM prefer a pattern p which does not violate any head constraint over those which violate a head constraint
in 1degour prototype implementation was based on the earley algorithm since this does not require lexicalization of cfg rules
by defining a few such notations these patterns can be successfully converted into the formal representations defined in this section
some light verb phrases can not be correctly translated without exchanging several feature values between the verb and its object
integration of translation patterns with translation examples or bilingual corpora is the most important extension of our framework
figure i shows a sample translation of the input he knows me well using the following patterns
suppose that the former is stronger than the latter
example NUM we con sider the following sentences
ambiguities a rc expressed by disjunct ions
for example consider the following two grammarital preferences
we introduced a central name server in order to store the components acting in an application and to be able to service requests for the creation of channels and such
due to the inability to remove paths in adwmee that can not be pursued fln ther at a late stage of operation the input to the syntactic parser grows enormously
we are going to describe the design and implementatior of a connnuniealion system l or large ai projects capable of supporting various software components in a heterogeneous hardware and programming language environment
we have presented the concepts and implementation of a communication system designed for use in large ai systems which nowadays are typically built to operate in a distributed manner within local networks of workstations
in addition to these systems we will introduce our own prototype system ilex NUM which is being developed specifically to examine issues arising in the domain of dynamic hypertext
a a features exploiting lcb he search i rol oxtlo s
lomta is a large scale natural language engineering nle system
NUM NUM representation of belief and intensional knowledge
distributedness and non linearity of lolita s semantic network
that tensionality and belief are independent
a control for each node specifies its type
in conventional hypertext the author selects content and connectivity and then retires the user is free to sample the hyper document however they wish
be interesting the visitor is free to ask about whichever objects take his fancy if an object does not hold his interest he will move on to another or finish
we conclude by indicating that the major benefit of such systems could be in the way that they combine flexibility with the illusion of user control
category based NUM fold cross validation was conducted for the category based and cluster based strategies in that NUM NUM nouns were randomly divided into NUM groups and one group of nouns was used for test data while the rest was used for training
when fishing on an ordinary river an angler casts their line and occasionally if they do it well enough and wait long enough they catch a fish
lemma NUM if g is a proper consistent scfg without useless nonterminals then the series for rl and rt as defined above converge to finite non negative values
the correct forward probabilities are obtained as a sum of infinitely many terms accounting for all possible paths of length NUM
that way it is possible to perform efficient earley parsing with integrated joint probability computation directly on weighted lattices describing ambiguous inputs
the two columns to the right in b list the forward and inner probabilities respectively for each state
we now formally define the relation between nonterminals mediated by unit productions analogous to the left corner relation
because the computation of probabilities already includes chains of unit productions states derived from such productions need not be queued which also ensures that the iteration terminates
fact t hat the oncel t of produ t is harder to eirt mnscril e
note that for simple non recursive udrss c NUM defines a partition 12the definition below ignores subordination constraints
nlg has often been viewed as a two step process
since the two sentences are of the same length an l involve the same set of semantic relationships the ditliculty in rmderstan ling NUM a can only be attributed to its syntactic structure
yet this information appears fairly late in the sentence
this said however they differ with respect to the semantics assigned to the underspecified representations aq and an
just as in the baseline system we rely on the language and translation models to take up the slack in place of an explicit grammar
using mutual information as compatibility values gives better results
so experiments with only hand written constraints are not performed
NUM NUM sij inf r rgrij
s s choice goes downward and r s choice upward without their initially knowing the other s choice
this is because cs cn is not a sufficient condition for the success of communication in that case
in english this ranking is determined by grammatical functions of the expressions in the utterance as below
second common belief on the game is a simple means to obtain common belief on the communicated content
meaning game captures nonnatural meaning in the restricted sense which obtains in basically all the cases of natural language communication
since our account is very general in nature however it should apply to language as a whole
pl and p2 are the prior probabilities of references to fred and max in u2 respectively
for instance there may well be a body of roughly correct stable common sense knowledge about the correlation between the competence of workers and the degree of effort they make to have higher education about how much an employer will offer to an employee with a certain competence and so on
for instance if a semantic content c is referred to by a message with a low cost then the probability of reference to c may increase as a sort of accommodation NUM because a reference by a lightweight message presupposes high prior probability of reference as discussed in section NUM
naive algorithm caused thrashing for storage in gc it is pointless to compare those tigures simply
at this NUM oint let us focus on the estimation above since it is only a NUM est case forecast
the requirements of incrementality interactivity and efficient communication show that our approach does not ernulate the description by analysis methodology in syntax semantics interfaces on the basis of codescriptive grmnmars
in contrast to a pure cf grammar with finitely many terminal nonterminals the evaluation process must not terminate due to eoreferenee constraints within feature structures
this protocol allows the parsers to efficiently exchange information about the structure of their chart without having to deal with explicit analysis results as feature structures
structures lmforehaml we can elnl loy type exl an sion to let syn or si m unexpan le l
since unification based formalisms are monotonic large structures are built up and have to undergo all the steps of unification copying and undoing in the processor
this illustrates that number of sentences NUM average ler gth NUM NUM in 1ran autonomous mode semqa the results tbr sem patwer as quasi autonomous semantic parser
we call even estimate the st eedup ill the best case viz quasi linear w r t input structure if only conjunctive structures are used
more specitically given part of an utterance u with semantic representation sern and loci f1 b we require that the following equation the ground equation be solved sere gd l t t assuming the typed a calculus as our semantic representation language this equation can be solved by huet s algorithm cf
for instance the fsv of la is defined as lb the set of properties of the form like lug y where y is an individual in what follows focus is indica ted using upper case we also follow montague s convention that for any type d is the set of objects of type r and wff is the set of wits of type r
figure NUM states and dfss in tim la in figure NUM
marion expressed by some section of serene can be unsound with re spect to the fn l semanl ic net
sere net is graph of nodes md arcs which degan i o rea d lraversed in oil her lir0clion
figure NUM wordnet senses for the nouns door
on top of this we use the following heuristic using context statistics to eliminate any further ambiguities
glosses are given as linear feature value sequences corresponding to the morphemes which are not shown
the tokens considered are those that are generated after morphological analysis unknown word processing and any lexical coalescing is done
the rules imposing constraints can also be represented as transducers which increment the votes of the matching transi
the contribution of fi vi in the vote of a constraint depends on a number of factors NUM
NUM in this case the weight of the feature constraint is w vi NUM
in the following sections we present an overview of the morphological disambiguation problem highlighted with examples from turkish
ve will call the middle phase in our dialogues the task performance phase since it is not always a negotiation per se
where as muci information as possible is collected before proceeding on to disambiguation rather than restricting the parser s search earlier on
if we are in parsing tile segment it s twelve fifteen and our only source of information is the previous segment
disco trse processing has frequently made use of equeuces of speech acts as they occur in the dialogue through bigram probabilities of occurrences
for example in sentence NUM above it turns out that the program uses safety as evidence for choosing job because job safety is a frequent collocation but this is the wrong sense of job
writing tile appropriate grammars and deciding on the set of speech acts for this domain is also an important part of this project
the use of statistical information in different parts of the processing such as the arcs of the fsm could enhance performance
in the table ci and leg indicate the numbers of ellis ters and examples respectively
9the criterion of this hand classification is taken from the existing japanese dictionaries for human use and the hand compiled japanese case frame dictionary classification method
complete i combines inactive charts ending at i with active charts that look for the inactive charts at position i to produce a new collection of active and inactive charts
we intend to augment and refine the list of features discussed here and hope to use them in understanding applical ions as well as generation applications
unc is perhaps a less felicitous name because we certainly do n t mean that the agent may perform actions while being unconscious
even the larger works e.g. the cookbooks and the do it yourself manuals are collections of the work of multiple authors
note that the table entry for conscious neg tc is NUM indicating that there were no examples marked as both con and neg tc
the conception of these features was inspired by the hypothesis put forward in section NUM as we will briefly discuss below
in our analysis we have attempted to discover and to empirically verify correlations between tile function features and the form feature
in this paper we define the notion of a preventative expression and discuss a corpus study of such expressions in instructional text
the study highlighted correlations between flmctional features and grammatical form tim sort of correlations usefld in i oth interpretation and generation
currently these features are set mammlly i y the user as they are too ditticult t o derive automatically
studies such as this have been done before in computational linguistics although not to our knowledge on preventative expressions
the number of active pairs is therefore t t
therefore rule rl is not considered for application to c3
the statement can be shown by proving the following claim
in fact the proposed preprocessing can at worst double ha
all the remaining matching nodes are inserted in the second set
there is in fact no arc in the complete lattice between nodes k NUM and d NUM because there is no cd kd mapping anywhere in either dictionary
dedina and nusbaum s strategy ol performing substring matching only over a restricted range the number of matching comparisons is equal to the difference in length between the input string and lexical entry is at the root of this problem
the pronunciation for a novel word not in the lexicon however is derived not by the application of abstract letter to sound rules hut is assembled from the known pronunciations of words that it resembles
instead it appears that words and pseudowords are pronounced using similar kinds of orthographic and phonological knowledge the pronunciation of words that share orthographic features with them and specific spelling to sound rules for multiletter spelling patterns
for the sull NUM test set our expectation of poorer performance because of the larger test set and inconsistency between of dialect between the target pronunciations and the lexical databases is borne out for webster s dictionary
this is not to say that sullivan and damper s system will necessarily produce the correct output here it ahnost certainly will not because of the rarity of the c k mapping in the d context
dedina and nusbaum acknowledge the crude natnre of their alignment procedure saying it was carried out by a simple lisp program that only uses knowledge about which phonemes are consonants and which are w wels
in this case the path through the e NUM node has a product score of NUM x NUM NUM for the pronunciation fed which considerably exceeds the score of NUM for fd
in japanese the antecedent part can be syntactically determined so far as the topic phrase is expressed with the topic marker
finally a discourse relation expressed by a subordinate conjunction node can be found in fig NUM too NUM
null the main focus of the project is on translation from german to english but it also treats that from japanese to english
we interpret them as relations between two drss consisting of restriction the antecedent part and scope the conclusion part
discourse relation elements can be also classified according to the anaphoricity of the elements expressing the antecedent part and those expressing the conclusion part
h5 will be bound to a drs which is constructed out of the sentence subordinated to noda that is the whole sentence
these scopal relations are at least theoretically able to be forced onto the sentence in fig NUM see see NUM
a hole will be bound by means of a plugging function to a standard label which stands for a drs of a certain element
in the spoken language machine translation project verbmobil the semantic formalism language for underspecified discourse representation structures lud is used
moreover it is also predicted that the reversed binding configuration where the reflexive would occur as the oblique complement x will be ungrammatical
then l t c l g is undecidable
pattern based context free grammars pcfg consists of a set of translation patterns
the ambiguous classes need to be removed altogether while the ones with mixed ambiguous and polllsemous lexical items are to be weeded out carefully
these representations are based on generative lexicon theory gps using qualia roles and dotted types pustejovsky 19os
a variation on this would be to compare texts using the loglikelihood applied to bigrams that are not necessarily adjacent i.e. counting occurrences of wordl and word2 within a limiting distance of each other
it is a threelayered approach with a de facto standard network layer pvm core routines and interfaces to live different programming languages together with sul port l or the transparent exchange of complex data types
the exlmrinmntm system architecture is shown in fig NUM it consists off several modules interconnected by t lnain da tlt path that delivers resuits according to the standard linguistic hie archy viz
in the case of integration of a word into a parse a ranking is produced which incorporates values from a statistical n gram language model and a stochastic unifica ion grammar which models the probability of a syntactic derivation
if one wants to study only some parts of the system it is t ossib e to start the al l li ation containing only a subset of the existing components e.g.
they can NUM e used to separate data st cants dora control messages or may NUM c configured it various ways e.g. by switching off lcb he x1 t lcb encoding to speed up message passing
in this case l oth eoml onents are started and we are at it to select fl om the gui which of both onq o ilents we a tllally wallt to lse
all the comf onents not selected are automatically replaced by stut modules so there is no need to change source code and recompile the components ew n if data is sent NUM o a nou existent component
col en et al t9 NUM can not be realized with our mechanis nl NUM but the range of cases where ici can NUM e applied is broader than this
ill addition the actual nature of each text is for sure an impommt factor difficult to measure which could account for the different behawfiur on its own
conceptual distance between two concepts is defined in irada et al NUM as the length of the shortest path that connects the concepts in a hierarchical semantic net
precision that is the percentage of actual answers which were correct and recall that is the percentage of possible answers which were correct are given in terms of polysemous nouns only
results are promising considering the difficnlty of the task free running text large number of senses per word in wordnet and the htck o1 any discourse structure of the texts
on the contrary we tested our method with all the nouns in a subset of an unfestricted public domain corpus more than NUM NUM words making fine grained distinctions among all the senses in wordnct
we applied the algorithm sussna considers best constraint for the first NUM nouns tile optimal window size according to his experiments of file br r05 had to deal with more than NUM NUM synset pairs
whilc the sense level gives a fine graded measure of the algorithm the file level gives an indication of the perl ormance if we were interested in a less sharp level of disambiguation
the algorithm proceeds then to compute the density for the remaining senses in the lattice and continues to disambiguate the nouns left in w back to steps NUM NUM and NUM
figure NUM genre related differences in the polarity system for constraint
figure NUM genre related differences in the mood system for substep
NUM furthermore we know that macintosh documentation undergoes thorough local quality control
procedure the top level gom of the user is expressed as a nominmisation
these actions are mostly materim directed actions and there are no causatives
figure NUM presents the reaiisations by genre of the polarity system for constraint
we examined the correlations between lexico grammatical realisations and task elements and communicative purpose
each unit is taken to be the expression of a single task element
choices made in each stratum constrain the choices available in the stratum beneath
we coded for sub categories of material mental verbal and relational processes
model which can inl erl rec a set of events particuhu ly iu the area of nmlti mo hd interfa ce
this would require con putating the string s reverse before annotating the constraint list
though trivial in the sense of being about mere recognition this is nonetheless interesting
in many current systems a mixture of these two methods is employed
v is the visitor and c is the curator
thus the cross serial dependencies need n t cost the worst ease complexity for parsing indexed or mildly ecrutext sensitive languages
we have implemented tile interpreter in a chart parser that can be used in either top down or bottom up fashion
a generalization of the recognizer method can be used inside a parsing approach as well
suppose instead that we allow ww n to be parsed without using tile metagrammatical method
then the sentence planner sends the semantic representation and the information strncture it has determined to the sentence realization component for turkish
determining the topic and focns is fairly easy in the context of a simple question however it is much more complica ted
if we look for an analogy in the real world to the kind of interface we are aiming for an interesting candidate is the dialogue between a museum tour guide and a browsing visitor
thus NUM denot es the lass of languages general ed by type NUM grammars
this paper concentrates on how to determine the is from contextual information using centering old vs new information and contrastiveness
c yes points out and describes two other brooches
this one was made by roger morris
for example all six permutations of a transitive sentence are grammatical in turkish although sov is the most common
our spirit is so twisted torn because of self out of its right center god and rooted in the flesh the old life is so foul in the sight of god that no patchwork no mere polishing up no amount of varnish will do
katz principled disambiguation in which old and young modify the same noun e.g. man old is thereby interpretable as not young in those in which old and new both modify a noun e.g. house old is thereby interpretable as not new
in this paper we disambiguate relative to a pair of word sense groups operationally by disambiguating relative to sense specific antonyms an old man for example is a man who is not young and an old house is a house that is not new
the semantics of noun senses therefore relate more specifically and directly to adjective senses than do nouns themselves in fact NUM NUM of the NUM cases not covered by the rules of table NUM are resolved when these broader semantic structures are used
we therefore discriminate between just these two sets of senses which constitute the great majority of instances of the targets and we exclude from both investigation and evaluation all instances in which the sense of the target does not fall in one of these two groups
but suitably constrained either to subsets in which decoration is not a functional value e.g. military industrial equipment or by treating feature combinations that include e.g. decorative relevance it would be possible if not ideal to handle such nouns in terms of attribute values
this adjustment for bias requires that we know the sense distribution of the target adjectives in the corpus to which the indicators are intended to apply and the number of instances in the subcorpora in which each potential indicator noun is modified by the target adjective in each of its senses
but the weight of an object is intrinsically relevant and its color irrelevant to the lifting of hat object a reference to the lifting of a light n in the not dark sense is likely to be misconstrued unless additional cues to interpretation are provided
this unrepresentativeness also introduces bias into the NUM if a pair occurs only once there is no opportunity for its target adjective to appear sometimes in one sense and sometimes in another so such pairs can not be used in estimating the consistency of sense selection by nouns
by contrast the second french option is based on a complex correspondence between the predicate fill and a multi lexemic structure procdder it remplissage
besides the equivalent expression in the target language does not always exist which means that even more complex correspondences should be found
for example in 4f NUM the manner attribute of the cleaning action is expressed as an adjective since this action is nominalised
the starting point is training text which has been pre tagged with the locations of all proper names
knowledge incorporated into the framework is based on a set of measurable linguistic characteristics or features
the first two e e j and j e j have been completed j j j is in progress
once built the trees are all applied individually and then the results are merged
hand coded heuristics can be developed to achieve high accuracy however this approach lacks portability
in addition ambiguities with delimitation are handled by including other predictive features within the templates
priorities are determined based on the independent accuracy of each tree
hand analysis of results leads to the discovery of new features
names are delimited using a set of pos based hand coded templates
proper names represent a unique challenge for mt and ir systems
to make this argument more precise we introduce mutual information s follows
7v effectuer une ventilation s che du rdacteur
proceeds with the filling of the hydraulic reservoir
the incorporated argument does not always hold the same semantic role
figure l an illustration of operator verb simple verb selections
4f effeetuer un nettoyage soignc du corps du filtre
however the way these basic mechanisms are combined is language specific
keywords multilingual generation lexical choice controlled languages
carry out the bleeding of suction lines
carefully clean the body of the filter
the degree of syntactic disconnectedness of a sentence is defined as the least number c of connected segments into which the sentence can be partitioned
incompleteness of the syntactic dictionary is overcome by assigning standard entries to the words absent from it but present in the morphological dictionary
nevertheless experiments show that if supplied with a large morphological dictionary the corrector even in its current state could effectively process real texts
the parsing i.e. constructing fragments by the bottom up procedure is performed in three stages in the order of decreasing predictability of syntactic links
in the case of correcting agreement crrors changes concern only word forms while the lexical content and word order of the sentence do not vary
for this structure the bottom up parsing is performed and syntactic structures are found that contain minimal number of changes in comparison with the original sentence
when they modify the same noun in a sentence this is the usual case in sentences in which they both occur this attribute is virtually ensured of applying in a consistent way to both instances of the noun
this work was made possible in part through a grant by the canadian natural sciences and engineering research council nserc
this difference in perspective is important for a writer especially when NUM trying to convey more subjective messages NUM
these factors influence the structure and contents of a statistical report and have to be looked at simultaneously in order to be effective
scholarships were also given by nserc and the fonds pour la formation de chercheurs et l alde ps la recherche fcar
first in their work the most common words are estimated individually and the less common ones are put together in their respective ambiguity classes in our work every word is equally treated by its respective genotype
since negative constraints such as article followed by verb reflect n grams that are impossible linguistically and therefore have an expected frequency of appearance equal to NUM we assign them a very high cost note that in order to keep the graph connected we can not assign a cost of x
from unseen pairs of words tags for a given word such as marine that can have NUM possible tags if only the instances marine adj fem sing marine noun fem sing are found in the training corpus one could assume that the remaining unseen instances have a much lower probabifity
the cheapest path for tagging the sequence of two genotypes p r jmp nmp can go either other case unigrams the cheapest path or the lowest cost includes the two transitions p and limp for a total cost of NUM NUM NUM NUM NUM NUM
verb 1st person singular present indicative vlsps verb 1st person singular present subjunctive v2spm verb 2nd person singular present imperative v3spi verb 3rd person singular present indicative v3sps verb 3rd person singular present subjunctive
moreover categories that can be very ambiguous such as coordinating conjunctions subordinating conjunctions relative and interrogative pronouns which tend to be collapsed consequently the disambiguation is simplified and results can not be compared
the top part shows how the genotype bigram p r jmp nmp can be tagged as a sequence of two unigrams the bottom part uses one bigram to tag it
the selected transition is the one with the lowest cost the example in table NUM illustrates the computation of costs with p nmp the selected tagging in bold
his m w be illustrated by the s structure 7b of sentence 7a 7b inky also be ret reseuted as the labeled bracketing giwm ill 7c NUM NUM a julie regarde marc indies looks at marc
thus each simple sentence introduces into the r NUM resentation at least one discourse referent either an cv ili or i stat corresponding to tim vent u lit y denoted by l h
we are mainly concerned with two of the four levels of representation of gb namely d structure and s structure
the aspeetual dimension of agr the previous clause structure improves significantly the correspondence between syntactic representation and semantic interpretation
in french this category contains the negative item ne seen as a weak affix lacking morphological stress
a or 1ing to the secon view negativ scil rcb iic rcb s co ivcy some kind of posil ivc information at the semantic level they denoi e a certmn kind of eventuality
with this definition in mind we think that at least in the case of ps negation over events is used mostly to convey something like nothing changed or the expected event did n t occur and there is in this case no event denoted by tile sentence
NUM NUM planning utterances based on tile fine structure of discourse an utterance pbm is elaborated using action schemata and decomposition methods
they can pick up a constituent of an action and describe it before describing the whole content of the action
we see typical ease in tile relation l etween NUM and NUM in figure NUM
the satellite describes the circumstances where the m cleus is interpreted such as the preconditions of a domain action
according to utterance plans the utterance controller sends linguistic expressions to tile text to speech converter
second circumstance is the relation between two segments a nucleus and a satellite
expression cont x y means that the content of x is represented as a set yof literals
communicative goals used here are persuaded plan p dialogue partner is pershaded to adopt dommn plan p
a map including NUM h cations such as station and NUM railroad lines w s used
pragmatic constraint el was used ill e2 uq explained in section NUM NUM
each markov process whose probabilities depend on the word i and its tag begins in a specim stai lcb t state the symbols it generates are added as i s children from closest to farthest until it re ches the stop state q he process recurses for each child so generated
then the probability of drawing a given parse fi om the population is NUM where kid i c denotes the cthclosest right child of word i and where kid i o start and kid i NUM ight kids i stop
in principle one couht model the distribution of dependency l arses l ur novel parsing algorithm a so rescues depen dency from certain criticisins l ependency granlmars are not lexicm and as far as we know lacl a parsing algorithm of efficiency compara ble to link grammars
link when a span constitutes a parse of the whole input sentence its score as just computed proves to be the parse probability conditional on the tree root eos under model c the highest probability parse can therefore be built by dynamic programming where we build and retain the highestscoring span of each signature
mdi stipulates that the best probability model for given data is that model which requires the least cod length or encoding of the model itself as well as the giwql data relative to it a
proteus syntactic grammars consist primarily of two rule types context free rules written in bnf notation and restriction rules written in a high level algorithmic language
the original koalas interface consisisted of a mouse sensitive simulated radar screen with a conventional graphical user interface composed of command pushbuttons dialog boxes and scrolling display windows
database query is used both in answering explicit interrogatives which fighters are n t holding cap station as well as dereferencing qualified nps moving aircraft
the nature of that interface depends on whether nautilus and the target are running in the same lisp process or as separate unix processes possibly on different machines
we consider here the general case for the tts translation problem in which the order of application of several instances of rule r to a tree can affect the final result of the rewriting
interrob is a new project exploring the integration of spoken and gestural inputs to a pair of mobile robots with rangefinder vision capability
we write mij to denote the j th node in a post order enumeration of the nodes of ci NUM i NUM and NUM j NUM assume that ci is the input tree
references to hypothetical entities are resolved by having focal dynamically consult the application database for the current object population at the time the phrase was uttered
NUM overlook avenue s w washington dc NUM NUM usa wauchope severett dennisp i marsh aic
apart from lexical and ontological coverage the depth and breadth of the meaning representations constructed by a system are good indicators of the overall semantic coverage of the system
in the interests of space we consider the above criteria for measuring semantic coverage but only provide brief summaries of how each system or approach is located along the dimensions of depth breadth and size
special problems arise in the extension of these algorithms to the possibly infinite domain of feature structures
a block is a set of all cells covering a particular input subsequence
it is important to keep clear the role of the position structure grammar
the general condition is a kind of markov property
computing optimal descriptions for optimality theory grammars with context free position structures
that idea can be extended to the context free case as follows
nevertheless locality is a sufficient but not necessary restriction for the applicability of this algorithm
one other useful partition of the dp table is into blocks
the first index identifying the cell x indicates the cell category of the cell
thus more than one pass through the overparsing operations for a block may be necessary
this is obvious in it is one of the neighbor s dogs and also in lie does not have children only one
we are concerned here only with the updating of a knowledge ase containing the knowledge valid for a discourse at a given time of its progression
in future work we will perform corpus analysis for additional pragmatic operators and extend the prototype implementation of our analogical speech translation system to cover these phenomena
the reader is referred to the above cited papers among others for more extensive justification
the times are somewhat slow for the larger trees but still acceptable for off line compilation
table NUM regular expression interpretation of the decisions involved in going from the root node to leaf node
the authors wish to thank fernando pereira mehryar mohri and two anonymous referees for useful comments
indeed our decision tree represents neither more nor less than a set of weighted two level rules
use of a tree directly requires a special purpose interpreter which is much less flexible
much attention has been devoted recently to methods for inferring linguistic models from data
to see how this works let us return to the example in figure NUM
see table NUM for an explanation of the symbols used in figure NUM
evaluations show that a language model trained on only NUM million words can perform better than one trained on a corpus of over NUM times that size
what is needed therefore is a test that makes few assumptions about the distributions of the underlying data but provides a directly usable measure of similarity
however dynamic lms still need a set of static baseline probabifities so the email lm may present the best starting point for this
secondly the email lm may be better because it wastes less probability mass on n grams that never actually occur in the test data
it is interesting to note also that this threshold is seen to vary according to the level of similarity between the adaptation domainspecific corpus and the background general corpus
the semantic representation is then sent to the sentence planner for turkish
i presented algorithms for deterndning the topic and the focus of the sentence
subjects are assllllled to e lllore salient than objects
sations contemporary novels and adult speedl from the childes corpus
if there are any discourse new entities i.e.
yesterday in the morning in the garden in 3mrkish
there is a talk at four
this notebk acc too much like l st ls i
c chris is giving the talk
these pp values are calculated using the 10k test data sample from the transcriptions of the vmr project
let i be the index of rule r under consideration
this work was funded in part by nsf grant iri NUM
each application of the word to word model can choose its own balance between link token precision and recall
neither model makes the correct links ddchapsnds screaming and ddmontde dangerous
for each kind of evaluation we have found one case where we can come close
the paraphrase category covers all link errors that resulted from paraphrases in the translation
the weather bitext was prepared at the university of montreal under the direction of richard kittredge
for these applications we have designed a fast algorithm for estimating word to word models of translational equivalence
l u v represents the likelihood that u and v can be mutual translations
NUM use the links to re estimate a a and the likelihood ratios
let r q q be a tree rewriting rule
we add qp to set lhs r and construct ag accordingly
we implemented the work reported here for spanish and english mt projects within the knowledge based paradigm
this process transfers relevant information and re indexes it according to the needs of generation
introduction there is no consensus on the type of lexicons which should be used for generators
advantages of a reversed lexicon a reversed lexicon has advantages beyond its practical use in generation
compounding this problem is the fact that analysis systems require different information than generation lexicons
for example a simple lexicon entry for the english word read might look like
read v NUM syn struc root read subj vari 0bj var2 sem struc
we outlined a relatively simple process for reversing analysis lexicons for eventual use in generation
testing the content of individual lexicon entries can also be made easier by generating sample sentences that conform to them
apart from this evaluation of semantic analyses can be difficult because it involves reading and understanding complex meaning representations
consider predicting the word barked in the sentence null the dog i heard yesterday barked again
p w t l l NUM wk wk ltk NUM
when xi assumes a word class or NUM as its value the corresponding model is called a class based model
or dendroid distrihution allowing for the possibility of learning one group of random variables to be completely independent of another
the best equivalence classification of the wktk word parse k prefix is yet to be determined
given this definition of dcps an application of a rule schema to two laughter signs d1 and d2 can be expressed in the following form where NUM r2 r rcb is a resolution sequence m head dtrnon head dtr d d2 ufs r u tur NUM u ur
more precisely it produces the unification results for a nod n in fj such that there is a path p paths n i such that the node reached by NUM is also defined in f2 or there is a path p paths n f1 prefixed by some p c rs or goals
n figure NUM our compiler generates NUM and NUM only from the lexical entry wrote without specifying the non head daughters indicated by the triangles in figure NUM since the non head daughters are tokenidentical with subcat values of the lexical entry for wrote the obtained skeletal parse tree contains the information that st takes a noun phrase as object and NUM selects another noun phrase
t denotes the i th parser operation carried out at position k in the word string
a simplifying assumption that is often made to deal with this difficulty is that random variables case slots are mutually independent
here the value of l indicates the presence of the case slot in question and NUM al sence
those argument phrases wh ich get assigned a theta role also get assigned a particular case nora aec etc
the concepts defined by means of these axioms are then used to specify the lexical entry of verschenken in its variant to live as a present
the paper is structured as follows drt s and set s basic motivations principles and formal means concerning lexical semantics are retraced n sections NUM and NUM
the inferential behaviom of leihen exemplified in section NUM motivates a forreal description that contains more than the basic distinctions provided by the partial lexical field to give
2more precisely there is a mapping front the set of variables into the set of nominal phrases more generally parts of speech f v b r
the signs changed are those of the disi osal and owner ship relations so and sl p looses the disposal and ownershi NUM of u and q gains them
theretbre one can expmm the lexicm enl ries rather turn onstrut ting l heln ea h and every time flom s ral h
they onsis of a i resul l ositional and an asserlx ric diseo wrs l cpresentation structure i r s
null NUM modes should be interpreted equally and indei mdently
to use various nixed modes ac cording to the sil ua tions
only whett multi imdal expressio is a lowe d
de fined corresponditlg to each mull i tnoda NUM expres sion
in such an interface a mouse click a spoken uttera nce
lewd NUM all mode inputs express identical contents i a eh
each mode input complements other mode inputs thus they express a single content
ill the level NUM section find appropriate termt NUM enclosed inside curly brackei s
defines t he procedures which iuterfa ce designers ma y follow in developing gra mma r based
firslly we briefly review t hi r lrchi m conslrainl logic l rogr ttn ming hcm language
the difference is that we let all t rcdical es vary and lnaximize preference rules whereas lifschitz mininfize abnormal predicates for prefercnc rlth s
t lsk NUM y a va rbmt of ircunmcription prioritized ci rcum scriptiou m car hy
rl hlls the foljlllcf t reference b c lll r 1lo hmer NUM licablc by the new scutcncc
wc uso m axla pbfl ion of kowmski s evenl cajculus kowalski and sergot NUM rcb
while there was no ordering preference in portuguese generation there is an ordering constraint on enablement imperatives expressing ed do not appear first
words we define correlation r jw ew between japanese word jw and english word ew as follows
here an np included in a larger np is rejected since only self contained nps qualify as nominal compounds
here n a and np stand for noun adjective and simple noun phrase respectively
so far we have only addressed nominal compounds simple noun phrases whose patterns arc given below
additionally the function words are useless as elements of co occurrence sets since they do not indicate specific contexts
while the examples shown here are for japanese and english the method is applicable to any pair of languages
the coverage is the proportion of the word correspondences in the corpus that are already contained in the bilingual dictionary
we take the strategy of selecting the mutually best matched pairs having no highly probable competitors
a pair of words can not be determined independently of the other possible pairs
bilingual dictionaries are essential componeuts for machine translation systems
bilingual documenls should be handled separately
hiromichi fujisawa for their constant support and encouragement
tile recall of the method is not high
however they are signficant in evaluating the effectiveness of the proposed correlation measure because the dictionary information regarding a particular pair of words does not contribute to the correlation between the pak itself
fig NUM associating words through contexts
the finding underlying our proposed method is as follows
that is feedback increases recall while preserving precision
these words do not contribute to the word pair correlations
this is the reason why the database blackboards are attached to only one of the four levels
in our experiment s our algorithm found severm additional non levin verbs that fell into this newly hypothesized lass including aspire attempt dare decide desire elect need and swear
gual dictionaries for spanish and arabic as a preliminary measure of success we picked out NUM l1 oce control vocabulary verbs i.e. primitive words used for defning dictionary entries and hand checked our results
in some cases the verb appears to have a related sense even though it appears in different classes
as one might expect throwing out the negative evidence degrades the usefulness of the signatures across the board
in the class based experiment we counted the percentage of semantic classes that had uniquely ide ntifying signatures
but the intensions of these functions are matters of significant theoretical investigation and although much has been accomplished in this rea the question of mapping syntax to semantics and vice versa is an open research topic
to compare these signatures with the previous verb based signatures it may be helpfnl to note that a verb based signature is the union of all of the class based signatures of the semantic classes that the verb appears m
these experiments served to validate levin s claim that verb semantics and syntactic behavior are predictably related and also demonstrated that a significant con ponent of any lexical acquisition program is the ability to perform word sense disambiguation
the research reported herein was supported in part by army l lcb esearch office contract i aal03 NUM c NUM through battelle corporation nsf nyi irl NUM alfred p sloan research fellow award br3336 and a
the following lig l where a is the start symbol
other non terminals are associated with independant stacks of bounded size
in a second phase the lig conditions are checked on this forest
moreover practical parsing times can be decreased by some statically performed computations
the overall goal of the menelas text understanding system was to build a normalised conceptual representation of the input text
the test consisted in code a ssignlne t t and answering a fix
NUM model join two arbitrary concepts in the two could be joined
pt p NUM in paths1 x paths2 the chain made
since types are organised in an is a hierarchy this knowledge is also inherited
in the semantic lexicon each word points to one or more conceptual representations
the semantic analyser precisely recovers these links thanks to the mechanism presented in this paper
however each grammatical relation may have conceptual preferences for types or for conceptual relations
the remaining problem ix to choose tile most appropriate chain to substitute for gr
in our approach the head concept type associated with an argument does not change
an evaluation of the proposed antecedents is performed using different kinds of criteria syntax semantics inferential etc the focusing algorithm makes use of data struc null tures i.e. the focus registers that represent the state of the focus the current focus cf representation alternate focus list afl that contains the other phrases of the sentence and the focus stack fs
they correspond respectively to the following surface sentences eel lafarge coppee said ee2 it would buy NUM percent in national gypsum the number two plasterboard company in the us ee3 a purchase which allows it to be present on the world s biggest plasterboard market
the other phases of the algorithm i.e. the basic focusing cycle are applied to the subsequent ees ee2 contains only one pronoun it which is resolved by the basic focusing cycle it in ee3 will be resolved in the same way
with intrasentential antecedents NUM in an embedded sentence with intrasentential in a simple NUM sentence with intersentential antecedents NUM our assumption means that while the prr pronouns may find their antecedents in an non embedded sentence e.g. sentences NUM and NUM the non prr pronouns can not
the associated syntax becomes the output to be presented to the user the fifth character in this line with pointer in the example
our project uses this grammar for output only because we have separately invested major efforts in the error correction system that has not been merged with the multimedia grammar
these sessions lasted about half an hour and students received less than NUM minutes of instruction about how to use the system
in many systems adding a mechanism for learning the user s preferences might involve adding code to a number of modules
the pointer complexity is also multiplied by log n where n is the number of items that are being distinguished from
however in the middle of the run the user begins taking a long time to respond to the use of highlighting
the system begins with a bias towards highlighting evidenced by its lower relative value as compared to that of using ordinals
such a segment is opened for a specific purpose may involve a series of interactions between participants and may be closed having successfuuy achieved the target subgoal
each operator accounts specifically for a syntactic item and simultaneously executes code in the semantic world which is appropriate for that syntax
controlling the movement requires that the system have domain information available to guide decisions concerning which directions may be good to take
again it is possible to imagine a counterexample for example dad stayed in the hilton like morn did in pittsburgh
in NUM a the reflexive is bound by a less oblique element in the subcat list in accordance with principle a but the construction is not acceptable
in some cases customisation is only in relation to content the page brings together information from various on line sources but presents it in a single standard way
some of the parameters are relatively wellunderstood and the state of the art in nlg is sufficient to allow us to manipulate them effectively in dynamic hypertext systems
a generation system in a hypertext environment faces a specific set of requirements here we discuss those requirements and the resources that can be provided to help meet them
we are proposing a new kind of river in which an angler can cast their line haphazardly and still pull out a perfect fish time after time
are unifiable with transitive and intransitive verbs respectively
for example for an enumerated type domain sub graph a single parameter specifies the list of values for the enumeration
the auxiliary properties override the ones that are inherited from the class thus allowing the tailoring of built in types in the input
since the variables are allocated sequentially their ordering is important and determines which variables will get the best encodings in problem situations
the task of agents is to perform operations on representations stored in blackboards
this allows the processing of answers that do not convey the expected information
in addition to feature structures they are able to leave disjunctions unresolved
the system consists of multiple blackboards each of which stores a separate database
as is the case with functions predicates are introduced by signatures
here too feature path application lcb obj path rcb NUM dst
the rules shown in figure NUM calculate the text containing the price information
from the remaining feature structures an underspecified feature structure is generated
our corpora for the study consisted of NUM exampies of generation and NUM examples of enablement for each of the three languages of study
the distribution of expressions among the two components ted and ing of the generation relation for portuguese is shown in figure NUM
in each case by performing the c t action or set of actions the user has automatically performed the fl action
second we can use our knowledge of how that relation is constituted and expressed in terms of syntax for marking the relation appropriately
three discourse relations are available for generation pur pose condition result and two for enablement purpose sequence
figures NUM and NUM show the relationships between semantic relation and syntax with an overlay of discourse rhetorical relations and discourse markers
there is also a constraint arising from the part of the semantic relation being expressed infinitives do not express the enabling action
what triggers these interpretations is constrained both by semantic content and by the conventions and syntactic resources available within the languages of study
recovering from speech recognition errors null there are various aspects to recovering from speech recognition errors for example in correcting phoneme sequences syllable sequences word sequences including compound words and collocations
we distinguish the following elements and provide examples of them in figure NUM NUM goals actions that users will adopt as goals and which motivate the use of a plan
we concentrate on the following ones NUM wa is a japanese topic marker and in general this marker can t e replaced by other case particles
we enumerated the realisations of those features that the first analysis had shown as marked and produced kwic NUM listings for each set of realisations
the domain model distinguishes constraints states which can not be achieved through planning and preconditions states which can be achieved through planning
neither task structure nor genre alone is sufficient to provide this control but taken together they offer a real prospect of adequate control over the output of a text generator
in some cases the type of task element is on its own sufficient to determine or at least strongly constrain its linguistic realisation
the results are best expressed using tables generated by wag given any system wag splits the codings into a number of sets one for each feature in that system
finally we have been assured by french users of the software that they consider this particular manual to be well written and to bear no unnatural trace of its origins
the finm step was therefore to look at the realisations of the task elements differentiated by genre in cases where the realisation was not strongly determined by the task element
in this paper we present a new approach for word sense disambiguation wsd using an exemplar based learning algorithm
similarly about NUM of all verb occurrences in any unrestricted text come from the set of NUM verbs chosen
conventional approachs such as knowledge based one can not easily handle continuous phenomena gradation of case role changing derivation of a metonymical relation and relationship between a topicalized word and the main predicate
the effectiveness of unsupervised learning on disambiguating words into the refined sense distinction of worbnet needs to be further investigated
the average accuracy of lexas over NUM random trials is NUM NUM and the standard deviation is NUM NUM
the choice of which sense definitions to use and according to which dictionary is agreed upon in advance
in each run we utilized only one knowledge source and compute the average classification accuracy and the standard deviation
this data set is almost two orders of magnitude larger in size than the above interest data set
our results indicate that exemplar based classification of word senses scales up quite well when tested on a large set of words
these are used to determine the pos of neighboring words and the verb object syntactic relation to form the features of examples
this characterization of the indicators for word senses also provides flexibility regarding sense definition
in both the old and the young man this was a breach of habit
short modifies sort concrete and was thus assigned the sense not long
overall coverage is NUM NUM see table NUM rather low for a disambiguation procedure
the effectiveness of one such alternative has already been demonstrated the special case of antonymic adjectives
otherwise inferring the correct sense for old involves a resolution of the function of the noun
the results of this test appear in table NUM under the heading indicator nouns
zaza still stood in the road on the wrong side of the car
the distribution of antonym related senses is given in the third column of table NUM
this procedure tests the sense specificity of the projected indicators in the NUM sentence samples
since the strategy described above is valid only for complex sentences which consist of a matrix clause and one or more subordinate clauses compound sentences which consist of main clauses must be considered
they can be rephrased in terms of functional criteria simply due to the fact that grammatical roles and the information structure patterns we defined unless marked coincide in these languages
we motivated our proposal by the constraints which hold for a free word order language german and derived our results from data intensive empirical studies of real texts of different types
the linear approach generates NUM additional errors in the anaphora resolution which are caused only by the ordering strategy to process each clause of sentences with the centering mechanism
NUM errors of the linear approach NUM errors of the approach which prefers inter sentential antecedents and NUM errors of the approach which prefers inter sentential antecedents can be avoided
if the resume mode is active switches itself the t3100sx automatically off NUM bei spiiterem einschalten des rechners arbeitet er sofort an der alten smile weiter
consider the example below in which the underlined word chunks construct a flexible collocation yif deg t f t x
their ninth is determine t hrase eorresl ondences i y using the phrase structures of i he two hmgua ges and oxisting bilingual dict iona ries
if auto talks between japan and the u NUM is extracted as a chunk japan and the u s is also tokyo forex NUM pm dollar at NUM NUM NUM NUM
our method consists of two steps NUM extracting useful word chunks by the word level sorting technique and NUM constructing bilingual collocations by combining these chunks
the proposed method which uses effective word level sorting not only extracts fixed collocations with high precision but also avoids the combinatorial explosion involved in searching flexible collocations
in this way long definitions have to have many words contributing to the total to be influential and short definitions are not penalized
so that if the word bank appeared three times in a given configuration we would add two to the overlap total
it is our belief that sense tagging can be carried out effectively by combining several simple independent methods and we include the design of such a tagger
as the corpus of parse examples grows and the system is trained on more and more data the system becomes more refined so that the supervisor has to overrule the system with decreasing frequency
the main purpose of learning here is to resolve translation ambiguities which arise for example when translating the english to knov to german wissen kennen or spanish saber conocer
to cope with the complexity of unrestricted text parse rules in any kind of formalism will have to consider a complex context with many different morphological syntactic or semantic features
the generation module orders the components of phrases adds appropriate punctuation and propagates morphologically relevant information in order to compute the proper form of surface words in the target language
plain sion structure list list tree decision structures with NUM training sentences and NUM features structions that just have not been seen before at all typically causing several erroneous parse decisions in a row
while this necessitates the involvement of a parsing supervisor for training we are able to perform deterministic parsing and get already very good test results for only NUM training sentences
number of correct constituents in system parse number of constituents in logged parse crossing brackets cr number of constituents which violate constituent boundaries with a constituent in the logged parse
this suggests that at least in the presence of other semantic features the subcategorization table does not play as critical a role in resolving structural ambiguity as might have been expected
traditional statistical techniques also use features but often have to sharply limit their number for some trigram approaches to three fairly simple features to avoid the loss of statistical significance
this can present a significant problem because even linguistically trained natural language developers have great difficulties writing and even more so extending explicit parse grammars covering a wide range of natural language
we have conducted some preliminary testing of this approach our tests were run on NUM handdisambiguated sentences from the wall street journal amounting to a NUM word corpus
additional lexical rules for recognizing entity names
table NUM summary of the muc NUM development process
a text is first broken up into sentences
corresponds to a word in the input sentence
if np1 in the pair np1 np2 is a pronoun bonus weights bi s are added to the weight of pair accordin g to the following rules a susan
a we assume two jobs are incompatible if a word appears in both job title s NUM abc announced that john smith president and ceo of xyz is named its next chairman
this research has also been partially supported b y
this single rule covers many kinds of expressions for specifying the locale of an organization such as 9a 9b 9c and 9d
the rule static is triggered by a person
a small number of the entries are manually coded
thus fin our experinmnts show that NUM produces better results than NUM
NUM pairs of bi gram position to capture word se quence informmtion
the details are exl lained in the following sections
winter inaker sotnce perrier s a according to french stock
note that a and NUM may contain repeated elements
m phrase or an n gram its follows
besides the effect of requiring a minimum of nondeterministic choices and thereby reducing the number of resolution steps to increase time efficiency the covariation encoding of lexical rules can result in an additional speedup since it reduces the space requirements of large grammars
we developed a compiler that takes as its input a set of lexical rules deduces the necessary transfer of properties not changed by the individual lexical rules and encodes the set of lexical rules and their interaction into definite relations constraining lexical entries
null discourse objects can be assigned unique constants i d that denote sets of discourse objects
NUM clearly sacrificing incrementality is not what should be desired although it may be acceptable for some applications
learning dependencies of criteria missing look ahead information could be acquired automatically by exploiting the derivational history of previously generated texts
figure NUM a sample production rule for a vp with an infinitive verb form placed at the end
i a t l grt und kuowlcdgt m t prt ferences
thus the concatenation of the left and right probability distributions for a type is what we call the environment of that type and we represent this by d in the subsequent part of this paper
figure NUM t ependency structure of NUM
for example figure NUM is the x bar structure of NUM
without loss of generality let us assume that w precedes its modifiers
however the lengths of some of the dependency links will change
figure NUM parse tree of 7a and 7b
on the structural complexity of natural language sentences
for evaluation purposes we conducted a word extraction ext eriment using the el l lcb
sign figure NUM an example of edr corpus probability vectors which consisted of all the con
in addition we need to minimize the effects of sample bias inherent in statistical estimates of this sort
we have described a new method to extract words from a corpus and estimate their poss using distributional analysis
this string occurs NUM times wittl NUM different characters appearing to its left and l0 to its right
in all cases structural complexity is reduced by the extraposition
consider the difference between 7a and 7b
a two pass retrieval has been implemented in smart to allow proper interpretations of such queries
in interactive mode a normal vector query can be entered using run command
construct the query and terminate the query with a period on a line by itself
the following were determined to be crucial in building an integrated extraction detection system NUM
documents not satisfying the query will be deemed to be non relevant for the query
pto s patent database show healthy NUM increase in average precision over baseline smart system
we have conducted a number of experiments to evaluate various modes of building an integrated detection extraction system
each automoton represents a single rule within the language with several related rules forming a package
a large variety of extraction capabilities best if could be generated rapidly on an ad hoc basis
we selected NUM queries out of NUM trec topics which explicitely mentioned some organizations by names
multiple inheritance in inheritance network terminology describes any situation where a node in an inheritance network inherits information from more than one other node in the network
section NUM describes the language more precisely its syntax inferential and default mechanisms and the use of abbreviatory variables
however we extend the notion of a sentence to include an abbreviatory convention for sets of statements relating to a single node
NUM word3 remains unchanged overriding the definition of syn form and so not requiring these additional features to be defined at all
l can you say more about that object
this association can be illustrated for the ambiguous adjective old which has senses roughly synonymous with aged long existing former used and obsolete using sentences from our experimental corpus see section NUM NUM
it is shown that these indicator nouns are also specific to the senses of the target adjectives in the corpus at large by using them successfully in a disambiguation procedure applied to NUM randomly selected sentences section NUM NUM
since the adjective is characterizing an action or state of affairs these cases can be subsumed under the activity or concrete semantic attributes discussed in section NUM NUM NUM as indicators of these senses of hard and right
however it is actually relevant not to the head of the noun phrase sort but rather to man animate so treated short would be correctly assigned to not tall
in these cases though part of the selected antecedent was not required by the vpe
a manual sampling of vpes in the brown corpus showed this to be true
NUM rcb alld correspond either to determiners label det or to argument positions relative to a predicate node other labels
for instance there exists a s fonn corresponding to the prefelted reading for fido visited most trashcans on every street which has every street
and that the result is ahl hate h l h2 peter hate peter h2 of type t
by the variable binding rnle for modiliers we need lirst to form the abstraction xh2 hate peter lt2 of type e t
the types given to the leaves of the tree are the usual functional types formed starting with e entities and t truth values
it may be remarked thai if in these rules we neglect the functions themselves NUM eft right resnlt and con
s forms which we introduce in this paper are a scoped vet sion of u fnrms and we define a compositional semantics mechanism for them
the tree of fig NUM corresponds to the lirst of ihese interpretations which is the preferred interpretation ik l sentence s i
now that we have the semamic translation hate peter h2 for the subtree pit we can compute the translation for the suhtree ph h2 woman
we start by giving a rect rsive definition of ibfs incomplete b forms that is b forms which may contain unresolved flee variables
first nouns are grouped into more than ten marked noun classes
the rule system is typically a combination of deletion and selection rules
third reduplication is a productive phenomenon
wa select NUM NUM el t ncl NUM
in intermediate sections there are rules which use larger structures for disambiguation
there are presently four sections of constraint rules in the rule file
the two interpretations have an equal lexical preference value and thus the preference of the two can not be determined by lpr
in fact it is more appropriate to treat the lexical preference as a kind of score representing the association between slots and their values
both represent an attachment of pp to vp but the length of vp of the former is NUM and that of the latter is i
where pi is the ith three word probability in the case frame of interpretation i and m the number of three word probabilities in it
we expect the effect of the use of the syntactic likelihood to become more significant when longer sentences are used in future analyses
one may argue that we could obtain the same number NUM accuracy if we were to employ a deterministic approach in implementing rap
where pi is the ith length probability in the syntactic tree of interpretation i and m the number of length probabilities in it
however the study poses a challenge in the sense that the notion of transitivity has previously been exemplified with relatively simple sentences presenting action sequences
the standard model of text retrieval is based on the identification of matching query document terms which are weighted according to their distribution throughout a text database
the highest scoring clauses either above a threshold value or on a comparative basis can then be identified as the basic clauses of a summary
for this assumption to form the basis of a practical implementation transitivity must be objectively defined and the definition must be able to be processed automatically
the fundamental task in automatic summarising is to identify the most important sections of a text so that these can be extracted and possibly modified to provide a summary
the value assigned to range can match number optionally with a comma as decimal point e.g.
note that regular expressions are specified as strings and must be quoted using pairs of double quotes
drei neunzehn zweiundzwanzig achthundert ffinflmdvierzig etc
we defined the proper noun regular expression to be nearly anything preceded by a capital
this kind of mapping rule allows a flow of information between text structures and linguistic structures
when the match is not entirely a proper noun the matching string can be edited
nlg can be viewed as the process of finding a linguistic form for a given conceptual fragment
much of the information to be ted is precisely intbrmation required by the lexicon
hence it is during the lexicalization phase that language can play this important role over thought
yet to walk expresses more of figure NUM is to be read counterclockwise
it is highly unlikely that the speaker has all this information available at the onset of verbalization
null please note that this example serves only for illustrative purposes
actually it expresses something more the notion of speed
before engaging in a conversation a message must be planned
we shall see this in the next section on lexical choice
as well as the case of the bilingual class class association score this definition only needs the set eg va for a japanese verb va not the whole japanese english parallel corpora
this is because our task is to discover strong association of an english lass and a japanese class in eg vj p rather than in the whole japanese english parallel corpora
let eg vg p be the set of bilingual surface case structures collected fronl the japanese english parallel corpora each element of which has a japanese verb vj and a japanese case marker p
increment the index i as i i NUM and go to step NUM otherwise set the number k of the subsets as k i and terminate the class flea
in the new definition we consider every possible set of pairs of a japanese case marker p and a japanese noun class c j instead of predetermining the most effective case marker
finally examples of a japanese polysemous verb collected from apanese l nglish parallel corpora are clivided into disjoint clusters according to those discovered sense clusters section NUM
for each case marker p in f s and its noun class c s there exists the same case marker p in e and its semantic babel semj is subordinate to ca i.e.
first we introduce a data structure which represents a set of pairs of japanese case marker p and a japanese noun class cj marked by p and call it japanese case class frame
sense classification of verbal polysemy based on bilingual class class association
the coluinn total c1 hand classif
moreover our parsing schema allow to avoid some useless computations
this text search tool is called spot
it needs to provide hundreds of users with access to this database
fill in the box customizable query entry forms
multiple languages in a single query
query generation tools to help non native speakers build queries in different languages
multi lingual query entry using nmsu s multi lingual text widget mutt
development is currently proceeding to interface spot to an excalibur conquest archival database
null allow users to perform external processes on portions of browsed text
the browser also displays the original document in its native script
the following subsections describe the design objectives and goals of spot
for each speaker i for each text j compare i s with the rest of speakers annotations and note down the average number of the matches the comparison result is shown in table NUM
having described the framework of the evaluation in this section we give details about the object systems to be compared in the evaluation work and the tasks to be performed in the evaluation work
for example in text NUM the numbers NUM NUM NUM and NUM occurring in the first 5th 8th and loth clauses respectively are initial references others are anaphors
although the average matching rates between the different rules and the speakers are lower than those from our previous experiments based on humangenerated texts this at least in part reflects considerable disagreement among the speakers themselves
even if the black box method is adopted however it is difficult to determine what is the appropriate input for generation and to be objective in evaluating the output text
to compensate for possible bias among the individual readers we sent the output texts to a group of readers for viewing and took the average of their outcomes as the measurement
all three systems produce the same result for text NUM and hence unsurprisingly all three systems have the same matching rate NUM as shown in table NUM
for example speaker NUM receives on average NUM matches for text NUM at the end of the table are the average numbers for the speakers agreement among themselves
moreover each linear so x derivation in l is the reverse of a string in ff dl NUM
the interaction of the agents and the blackboards is governed by a set of expert system style rules
however automating the process of concept identification in untbrmatted text has not been an easy task
when such naive spotter is ditlicult to come by one may resort to hand tagging
similarly the neg tc form is more likely to be used to prevent actions the reader is likely to execute unconsciously
these features are detailed now unaw is used when h is perceived to be unaware that a is bad
each author independently coded each of the features for all tile examples in tile sample
we settled on two values con scious and unc onscious
we have used the correlations discussed in this paper to build the text planning rules required to generate negative imperatives
we have found the kappa statistic critical in the definition of the features we coded see section NUM NUM
the first row in table NUM shows the frequency of occurrence for each of the grammatical forms we probed for
at generation time then drafter must be able to select the appropriate grammatical form for the preventative expression
functional features are the semantic features of the message being expressed and the pragmatic features of the context of communication
it is at l roximately NUM mb in size and is made entirely of written english instructional texts
towew r well the lal t er deal wilh only NUM arl of the probh m they l ry not to extract the mlwanl cd substrings of collocations
however for some sets of preferences this approach has proven to be sufficient and very useful
the success of this method depends on how well the derivation under construction fits with the sample data
both possibilities taken together allow a system that feeds tg NUM to specify linguistic criteria of preferred solutions to be generated first
for instance time and date descriptions encoded for the cosma domain can be reused in other tg NUM applications
we must leave it to future research to identify ard apply suitable learning algorithms to solving this problem
ideally these functions must implement a theory of how mutual dependencies of criteria should be dealt with
NUM a a maria falou corn o pedro acerca delei
second the elements of arg s value may have a non linear order
a maria falou acerca de si prsprioi com o pedro
one such case can be fbund in the context of russian passives
branching split obliqueness at the syntax semantics interface
zq rmmuns must be o bound
y is more oblique than x
i am grateful to hans uszkoreit and pahnira marrafa for their advice
NUM a tarooi ga zirooj ni aete zibun zisini j o hihans ase ta
similarly t is said to translate s iff there is a synchronized derivation sequence q for s such that t accepts s and every head and link constraint associated with the source and target cfg skeletons in q is satisfied
where obj is a local feature for head vps in liiss while obj is a local feature for vps in 13again these patterns can be mapped to a weakly equivalent set of cfg rules
we discuss major requirements for such tools including easy customization for diverse domains the efficiency of the translation algorithm and scalability incremental improvement in translation quality through user interaction and describe how our approach meets these requirements
a set t of translation patterns is said to accept an input s iff there is a derivation sequence q for s using the source cfg skeletons of t and every head constraint associated with the cfg skeletons in q is satisfied
a head is typically introduced NUM in preterminal rules such as leave v v partir where two verbs leave and partir are associated with the heads of the nonterminal symbol v
two open questions however have yet to be satisfactorily answered before we can confidently build commercial mt systems based on these approaches can the system be used for various domains without showing severe degradation of translation accuracy
this paper proposes the use of pattern based context free grammars as a basis for building machine translation mt systems which are now being adopted as personal tools by a broad range of users in the cyberspace society
12a similar preference can be defined for the target part of each pattern but we found many counterexamples where the number of nontermina symbols shows no specificity of the patterns in the target part of english to japanese translation patterns
in our prototype system each pattern has its original weight and according to the preference measurement described in the previous section a penalty is added to the weight to give the effective weight of the pattern in a particular context
new york and stock exchange should be extracted while york stock should not
null the particular structures found in sublanguage texts reflect very closely the structuring of a sublanguage s associated conceptual domain
in this t al er we are concerned wil h nested collocations
it is straightforward that in this case a appears in one only hmger candidate collocation
we focus on the problem of the extraction of those collocations we call nested collocations
it is not the goal of this paper to provide yet another definition of collocation
let us consider the string new york stock ex hange
the other NUM gram is not a candidate collocations it gets c value o
linguists have long been interested in collocations and the detinitions are nuiaerous and varied
it is the human judge that will give the tinal de ision
the rule builds up infinitely large subcategorization lists of which eventually only one is to be matched against the subcategorization list of e.g. the lexical entry for buys
intuitively understood filtering is reversed as binding information that normally becomes available as a result of top down evaluation is derived by bottom up evaluation of the definite clause characterization of filtering
as a result of the fact that there are no other magic s literals in the remainder of the magic compiled grammar the magic s rule can be discarded
NUM pn mary p p mary i0 n booklp p book
NUM magic np csem magic vp vform ssem vp p0 pl vform csemlargs ssem
NUM magic np csem magic s vform ssem vp pl p vform csem ssem
the tree on the right is the proof tree for the example sentence which is built up as a result of unifying in the derived magic facts when applying a particular rule
filter optimization often comprises an extension of a specific processing strategy such that it exploits specific knowledge about grammars and or the computational task s that one is using them for
it has the effect of dividing the evidence from a training instance across all possible categories for the words
been studied from the perspective of minimal addition to incorporate copy languages
we dist inguish between context free representability and contc xt free processing
table NUM models of grammar and computation
this is the tradeoff lletwe en doing things metagrainmatieally and directly
cross serial dependencies are not hard to process
tile context free languages have alre ady
another example comes from the recognition of rotated objects
interestingly if w is in ps2 and we use the metagrammatical parsing method then ww c also requires more processing time than ww for the same reason as the trivial case
however this is not the complete story since we have not accounted formally for the extra implicatures that the use of a blocked form conveys nor have we allowed for the generation of blocked forms apart from in the circumstances where the generator s lexicon omits the synonym
a sample corpus of news articles has been coded for transitivity
the following properties contribute to the individuation of an object
the difficulties encountered in this process will determine the basis for future automation
summaries are produced by extracting clauses according to transitivity scores
besides aiding in the development of a practical tool for learning phonological rules our results point to the use of constraints from universal grammar as a strong factor in the machine and possibly human learning of natural language phonology
we hope that further experimentation will lead to a way of expressing this language bias in our induction system
the improved algorithm induced a flapping transducer with the minimum number of states with as few as NUM samples
the algorithm for learning them is an extension of the ostia algorithm for learning general subsequential finite state transducers
the cost of insertions and deletions was NUM roughly one quarter the maximum possible substitution cost
this type of generalization can be accomplished by pruning the decision trees at each state of the machine
figure NUM shows the correct decision tree for flapping obtained by pruning the tree in figure NUM
essentially we consider languages xx homomorphic to ww where x can be either ps3 or ps2 and argue that the recognition for xx is no worse than worst case recognition for ps3 if x eps3 and no worse than the worst case for ps2 ifx eps2 even though xx is itself indexed
a left bracket i indicates the end of a complete left context
a regular relation is a mapping from one regular language to another one
NUM NUM inverse bidirectional and optional replacement
left and right contexts can be checked before or after the replacement
i.e. as not being adjacent to another empty string
the language described by the ui per art of a
a relation described by NUM could not accomplish this
the text is then added to the orthographic level of the discourse blackboard
a strong system of preferences is used which substautially reduces the number of arising fragments
the corrector begins its work with ordinary morphological analysis and parsing of the input sentence
NUM mel uk and pertsov NUM apresjan et al NUM
as only single distortions were considered it was fixed rma x NUM
the set of all homonyms built for a sentence is called its morphological structure morphs
transforming finite verbs into infinitives and vice versa is also regarded as semantically empty
a natural application of an agreement corrector is to process texts in computer editors
in a sense d is the distance between the fragment and the input sentence
notes in condition NUM t r and s denote parameters for situations relations about expecting and speakers respectively
this assumption leads to the misconception that irony is governed only by a simple inversion mechanism and thus it has no theoretical interest
d r examph l he fact that ira believes knows the above event is re t resente d
our theory allows us to rive plausible answers to what irony is how irony is recognized and what irony coinmuni ates
implicit communication can also be accomplished by utterances explicitly referring to the pleased emotion that speakers would experience if their failed expectation became true
the use of the interjection oh with a special tone of voice in 4a offers one typical example of this
that is an answer to q3 and then the hearer interprets understands the ironic utterance by adding that information to his her mental situation
as she looked out the window nancy slid NUM a it the weather is really ni e
example NUM nancy and jane were l lamfing a trip to the beach but that day was a coht and stormy one
since words are the linguistically significant basic elements that are entered in the lexicon and manipulated by grammar rules no language processing can be done unless words are identified
r the select ed expressiorts t ecollles tim seed for the specifiea tion of the desigm d t ult i
class sut class rela l ions based on sinq le lea jutes and el how llnct ions of t he objecl s
consider the example of a chiht who is using a nnlltinmdia encyclopedia system whicls provides a mix of speech recognition and language processing and a mouse
prolog predicate which detertnines the correct mea lfit g using cot text analysis whet liltere t
all hough a nuniber of luetliodologies ha ve been forinulaj ed l o buihl presenl u is by soflwa re
this can be arbitrarily hard especially as we consider that the number of utodalities wi l keep growing as user interface technology design comin ues
analysis whe the cond inat io t of gcnerajed tuea ui g of all modes still lacks htrort m tion
the mt techniques employed in the tools however are fairly conventional
at its simplest this approach involves a combination of manual inspection and regular expression searching to identify those parts of the background corpus that contain suitable material
pp thus measures the recognition difficulty of the text relative to the given lm and is measured by applying the model to a sample of test data
as mentioned above the vmr database is a collection of speech data with transcriptions of which the latter were used in the above evaluation
firstly there may be n grams in the email corpus that are simply not found in the bnc even though the bnc is NUM times larger
although the loglikelihood and rank correlation metrics both produce results that can look intuitively plausible this merely underlines the need for an objective thorough evaluation method
the top down approach assumes that the bnc classification system is perfect in that each text classified as belonging to a certain domain really belongs in that domain
initial explanations for this centred on the vocabulary since a higher incidence of out of vocabulary oov words can produce a lower pp but a higher wer
the second lm was buik from the whole of the bnc using the same vocabulary as the email lm in order to ensure consistency
unfortunately it was not possible to calculate the pp of the wsj lm due to the absence of a readily available version in the correct format
a discriminator trained on many types of text so as to be generic will not be particularly successful in any specific domain
there are several interfaces between the individual parts of the application which are used to forward results or to realize question answering behavior
a channel oriented model or interaction relations between software modules seemed to be the most suitable system for our needs
fur4pvm addresses components which are identical to processes for it by a task id that is assigned by the pwn daemon
the objects exchanged between semantics construction and transfer are relatively small but highly structured semantic representations with several embedded layers
on the other side it is possible to configure the usage of alternative components e.g. two gerlnan speech recognizers
one may specify routines to encode and decode userdefined data types which can then be transmitted just as the predefined scalar types
the feedback loop to the speech recognizer consists of information about the syntactic ranking of the parse each word is integrated into
a component desiring to take part in the communication activities of the application has to identify and register itself at the ils
the testbed manager tbm is used to start up the whole application and to distribute the processes of the application to the hosts of the network
table NUM contingency table for intentionality
do not scrub or wet mop the parquet
the mere fact that k may have a vahw
table NUM contingency table for awareness
in this paper we present a more systematic analysis
be careful not to drill through the pattern line
table NUM x NUM statistic and significance levels
table NUM l istribution of negative imperatives
to compute x NUM for the coded examples in our corpus we collected all the examples for which we agreed on both of the functional features i.e. intentionality and awareness
while instructional text has sparked much interest in both the semantics pragmatics community and the computational linguistics community little work on preventative expressions and in particular on negative imperatives has been done
in sentences like a number of companies sell and buy by computer NUM the number of three word probabilities in each of its respective case frames will be different
sometimes in the examples we draw trees in the usual way indicating each node with its label
the relationship between transformation based tree rewriting systems and standard term rewriting systems will be discussed in the final section
therefore we have improved the asymptotic time complexity of transformation based parsing of a factor between two to three orders of magnitude
an associative list state associates each node n of the rewritten input tree with the state reached by aa upon reading n
we consider ordered trees whose nodes are assigned labels over some finite alphabet e this set is denoted as et
the notion of transformation associated with a tts g e r is now introduced
as already discussed in section NUM the worst case condition is hardly met in natural language applications
the contribution of frequency is max lcb NUM NUM freq w where maxhxfreq x is a function of the five highest frequencies in max xtreq the corpus
although profit and medicine are not closely related semantically relative to a more balanced corpus than wsj their contexts in the wsj contain words that are similarly indicative of the sense of the target word
the weight of a word is a product of several factors frequency in the corpus the bias inherent in the training set distance from the target word and part of speech label NUM global frequency
first instead of tallying word statistics for the examples of each sense which may be unrefiable when the examples are few we collect sentence level statistics representing each sentence by the set of features it contains
the circularity of this definition is resolved by an iterative converging process in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the mrd
we assume for the sake of example that zoom in was recognized instead of the museum and that the semantic parser skips zoom in
coverage a lexicon of more then NUM NUM stem entries and an excellent speed NUM words sec without compound handling NUM words sec with compound processing where for each compound all lexically possible decompositions are computed
the research underlying this paper was supported by research grants from the german bundesministerium fiir bildung wissenschaft forschung und technologie bmbf to the dfki projects paradice fkz itw NUM and paradime fkz itw NUM
for example if np is used as a type name for nominal phrases then the output description of the above np recognition part is build item type np out list det adj noun
then using contextual information from unambiguously analyzed word forms filter rules are determined which are of the form change tag of word form from noun or verb to noun if the previous word is a determiner
a declarative specification tool for expressing finite state grammars for handling word groups and phrasal entities e.g. general nps pps or verb groups complex time and date expressions proper name expressions
conc star n mona cat det det NUM star mona cat adj adj mona cat n noun
first many of the partially recognized templates are part of coordinations including enumerations in which case several local templates share the same slot however this slot is only mentioned one time
the same blind test was also carried out for the date time and location subgrammar i.e. they have been run on the new corpus without any adaption to the specific domain knowledge
in future work we will investigate ineori orating additional aspects of centering theory including the tbrward looking centers list and the preferen e orderings on transitions
in tile secon NUM sentence john becomes the value of index NUM and also replaces 5ibm as the discourse center and thus john becomes the value of index NUM
alk la h in this lis ours h ira enl ity iill rodu e d
all four possibilities in fact occur as shown by the following examples NUM tom v loves in his cat
this requires contexts to change in a way that is somewhat foreign to the dynamic perspective a given position in tile ontext must be reassigned or shift its value
in dynamic semantics a discourse is viewed as a monotonic increase in information as discourse referents are constantly added to the domain of discourse
cat in tile first senteilce to m is the value of illdex NUM and ix also the discourse center i.e. the value of index NUM
in this paper i will implement a simplified version of the centering theory in a dynamic system and of phenomena involving sloppy identity in ellipsis and paycheck pronouns
boxes appearing in sequence can be m evged inlo a single box consisting of the union of the discourse markers ill the two boxes and the union of the conditions
this paper proposes a segmentation standard for chinese natural language processing
sproat and shih NUM and chert and liu NUM
the principles also provide a functional procedural algorithm for identifying segmentation units
there are two points worth remarking involving the above definition
the modularization of the components will facilitate revisions and maintenance in the future
segmentation standard for chinese natural language processing
for instance the necessity for lemmatization attests to the fact that some linguistically dependent units may have independent grammatical function and meaning and need to be treated as basic units in language processing e.g.
put the hydraulic system under pressure
12f mettre le circuit hydraulique sous pres8ion
the structure of this paper is as follows
these rules may roughly be described as follows
5f acedder d la soute artiste
4e carefully clean the filter body
both input and example expressions are matched after shallow syntactic analysis
since the viewpoints of a node are inherited by its children in many cases the existence of the connected nodes that include isa relationships is strong evidence for the viewpoints
the procedure consists NUM 1he following three steps step l i xtraclh n of viewl ohgs i or each node in isamap
t lcb s ik r qmsed a classtmse i a pproach in which sets o words are sed
for this lmrpose the functions can be exp nded to contaiu tit local conlexl o the word as augmeutations of the functions e.g.
in english a more polite way of phrasing the request can you do x for me
for the reasons outlined above it is important to handle pragmatic information in spoken language translation
the notion of a type in a feature structure refers to the fact that every feature structure is assigned a type from a type hierarchy
in these cases we j dge that the result of the automatic classification is correct when it corresponds to one of the domains where the document is cblssifted by editors
in our approach the maximum correct recognition scores for the editorial articles and the articles in scientific american in japanese are NUM and NUM respectively
i c ongs to figure NUM shows it procedure for the docuinent classification isili dolltaill specific kanji chara cters
to represent the features of an unclassified document and the NUM domains we used feature vectot s NUM and NUM respectively
because this book is manually classified into tile domain information science in tile n dc it is correct that the system classified this book into the information science
for example botany which is one of the authors specialties is also one of the subjects of the domain in the ndc
the features of doculnents and domains are rel resented using the tim ture space the axes of which are these domain specific kanji characters
NUM dept of electronms and comlnumcatlon kyoto university yoshida sa yo i yoto japan
figure NUM figure NUM and figure NUM describe the variations of the classification results with respect to the number of domain specific kanji characters
the reasoll why we use this encyclo imdia is thai it is lmblished in the electronic form and contains a gi oat liiiiill oi of articles
the lexicon uses a semantic sense as the basic unit and employs a multi tiered constraint structure for the resolution of syntactic information into the appropriate senses and or idiomatic usage
roles tiieme figure NUM l he simplified causation rule for intran sitive verbs constraints d ove
nor is there an ecd or cdo trigram with or without the right endpoint phonemes which could possibly bridge the gap
a direct version scores candidates using dedina and nusbaum s method with its two prioritised heuristics we call this model d n
temporary removal from the lexicon means that the pronunciation must be assembled by the analogy process rather that merely retrieved in its entirety
they also examine different ways of numerically ranking candidates taking into account probabilities estimates for the letter phoneme mappings used in the assembled pronunciation
the correct pronunciations are those read aloud by sullivan s NUM non phonetician subjects and transcribed by him as british received pronunciation
dedina and nusbaum s claim of NUM words correct amounts to just NUM errors NUM of which are the same as ours
for some lexical words no pronunciation at all was produced because there was no complete path from start to end in the lattice
one of the inputs for which no pronunciation was found is anecdote whose partial lattice is shown in fig NUM
finally there is a start node at position NUM and an end node at position one greater than the length of the input string
the arc is labeled with the phonemes intermediate between l m and pj in tim phoneme part of the matched substring
further pp testing possibly using the complete transcriptions of the vmr data is necessary to clarify this issue
which the larger lms waste probability mass on n grams that never actually occur in the test data
this process is usually performed off line i.e. independently of the speech reeogniser for which the models are intended
the speech part contains audio files for NUM speakers of which NUM were used in the current investigation
therefore the filter was applied to ignore eases where there were fewer than NUM NUM words in eornmon
however as before the number of common words is very small for some of the texts
word senses are not absolute or platonic but defined by a given lexicon as has been known for many years from early work on wsd even though the contrary seems widely believed it is very difficult to assign word occurrences to sense classes in any manner that is both general and determinate
NUM a je ne lui rfipondis pas
if an independent nhyp is computed for every concept in wordnet we call it local nhyp
in order to give an overall view of the performance we consider the average hehavior
therefore global nhyp is favored and was used in subsequent experiments
nevertheless in the rest of the results reported below meronymy and hypernymy were used
each sense of the words belongs to a subhierarchy of wordnct
figure NUM show that local nhyp performs only slightly better
a priori the more relations are taken in account e i
l ornlula i shows a lmralneter that was colnputed experimentally
for exainple the utteran c NUM that nlenlion theory caltllot ext lain alludes to candy s exi ectatioll NUM y refe rring to one of the conditions
furthermore wordnet NUM NUM is not a complete lexical database current version is NUM NUM
a fact event that candy eats dm pizza is represented as t eat x a where the situation t expresses the spatiotemporal location of that event
the fact that this genotype very ambiguous as a unigram NUM NUM can be disambiguated as a noun or adjective according to context at the trigram stage with NUM NUM accuracy demonstrates the strength of our approach
we added the extracted unknown words to the dictionary of the stochastic tagger where they are recorded with a frequency calculated by the following fo mula size size f c pos where size and size are the size of the edr corpus and the size of the scientific a merican corpus respectively
the cmculation of the enviromnent of an arbitrary string possible word in a corpus is basically identical to tire pos algorithm above except that because japanese has no blank space between words arr t a raw unsegmented corpus is used the extent of the environment is ambiguous
is assigned a specific probability by a slot based model
liially instances inl ercomlecl cd lit pot cnt ially mt inlalil ivc ways w have NUM r vi l NUM a c nlx lle NUM na m al l anguag cni inlx r ac for i m mli h r
in general however the doculnentation produeed by these systems is limited in two main ways it does not correspond to task oriented documentation which is however what end users re null quire and it is usually based on siint le template generation which does not allow flexibility with regard to the style of the text t rodueed or the language that is used
this indicates that the save as dialog box may be ope ned by either choosing the save option from the file melm choose save t tion or licking the save butttm on the tool bar click save h on
early in the i i lcb aftei lcb projee t we conducted interviews with technical authors me stly soft ware clocmnentation sl ecialists in order i t understand the docmnentation process as it currently exists to see if an authoring tool wouht be hell rcb tiff and if so how it inight be used
represented in the figure by the action node save l document is implemented in the knowledge base as a comple x of instances repres mting the act ion being tmrformed in this case saving tim agent who performs action the reader the t atient on whom the aetioll is performed the current doeunmnt etc
i l l halt lies vents as objects in the la nguag
in short our scheme consists of tile following three parts NUM a parser based on tim nondefeasiblity thesis NUM pragmatic constraints specific to linguistic expressions and NUM the general ontology of the worhl described by tnanuals
although of course not all the zero pronomls can t e solved with the constraints shown in the paper our examination for a lot of manual sentences shows that the constraints work very effectively and accurately in sentences with conditionals
for example itamo havc a pain is a non volitional verb otoso drop loose is a volitional verb which has also the non volitional use sagasu scarch is a volitional wm which has only the volitional use
although this kind of theory has a good point that it is independent of the type o17 discourse the linguistic constraints specitic to expressions like the pragmatic constraints l roposed by dohsaka or us are more accurate than theirs when the speeitlc constraints are applicable
in japanese simple operation procedures like those which do not inelude some conditions are often described as simple sentences with no subjects whose verbs arc of one of the following types the ru form the request form or the solicitation form
note that when only the matrix clause of NUM is use t as shown in NUM c can lie interpreted as either the hearer or the machine NUM gb e de lnas u
constraint NUM subject of sentence in the request form the subject of a sentence in either the requesl form or the solicitation form is the hearer manual sentences may have a kind of modality expressing the permission the possibility the obligation and so on
in some sense the resolution of zero pronouns referents especially the resolution of zero subject is the essential part of the knowledge extraction fi om jai atmse illanuals becanse once referents of zero prollotllls are identified we can use w rious methods already been l roposed to recognize the structure of sentence and to map it into the suitable knowledge representation
however even with this modification the mean rank remained as high as NUM NUM std dev NUM NUM
single words can be regarded as root only trees
during a run of the parser both forward and inner probabilities will be attached to each state and updated incrementally as new states are created through one of the three types of transitions
the standard earley algorithm together with the probability computations described in the previous section would be sufficient if it were n t for the problem of recursion in the prediction and completion steps
an existence proof for rl is given in appendix a appendix b NUM NUM shows how to speed up the computation of rl by inverting only a reduced version of the matrix i pl
the main problem with null productions is that they allow multiple prediction completion cycles in between scanning steps since null productions do not have to be matched against one or more input symbols
the expansion of x proceeded using the production x and has expanded the right hand side rhs up to the position indicated by the dot
if at any intermediate stage a state set remains empty because no states from the previous stage permit scanning the parse can be aborted because an impossible prefix has been detected
specifically the probability p associated with an lr
the following remarks assume familiarity with both approaches
i would like to thank paul smolensky for his valuable contributions and support
the domain planner includes NUM action schemata and NUM decomposition methods
it specifies how an action is decolnposed to a detailed phm
the problem solver tries to make a more concrete plan
this results ill the fine structure of diseom se
the constituents of a domain action are often referred to in its preconditions
with the constraints were contpared with those generated without them
pragmatic constraints are required to guarantee the relewmce of discourses
c3 be relevant according to the attentionm state
the m eleus describes a domain action or state
the utterance planner makes utterance plans to propose domain plans
wh can confl ine bigram and ti igranl infi omation in a back off mechanism use trigrams if available and bigrmns when not
p v t the lexical probability for the word represe tted by v to have t ag t
NUM NUM NUM the diificulty to state which is the omt atibility value for each constraint
the algorithm requires a way to compute which is the support for a wn iable label given the others and the constraints
several support functions are used in tire literature to define the support received by label j of variable i sij
the second formula increases weight for labels with support greater than NUM and decreases weight for those with support smaller than NUM
to this extent we have presented the relaxation labeling algorithm family and stated soine considerations to apply them to pos tagging
the training and test corpora will be the same for all taggerm null all results are given as precision percentages over ambiguous words
we performed the same experiments on three different corpora corpus sn spanish novel train 15kw test 2kw tag set size NUM
tfs are lisp functions defuns of the same name as the predicate taking keyword arguments corresponding to each of the predicate s semantic slots and exchanging appropriately coded information with the target application via so called interface functions described next
model objects have a tinsel semantic class attribute permissable identifying specifiers s s loveboat waypoint no NUM ntds icons and a marker indicating if the object represents a collection of unindividualized entities map rings aircraft trails
since the application is written in common lisp and so can run in the same process environment as nautilus we dispensed with independently modeling focal entities for the domain and just let the lace database objects serve as the extensions of referring expressions
since the system does not yet query information from the robots deictic reference must currently be resolved on the robot side rather than as in the systems described earlier by having nautilus choose from a set of candidates provided by the application
tinsel does contain some general rules for handling noun phrases however such as automatically attempting to interpret certain prepositional phrases as implicit be verb relative clauses the hammer on the table the hammer which is on the table etc
in the fourth project eucalyptus we developed versions for both approaches one where the application object code compiled from c is loaded into lisp and the ifs are foreign function calls and the other doing ipc message passing
statistical method of recognizing local cohesion in spoken dialogues
part c without local cohesion turn taking
sions in an utterance the first process in our method identifies the speech act expression in each of the utterances by matching the longest pattern with the words in a set of speech act expressions
arigatou gozai mashita thank you itadake masu ka requirement onegai shimasu requirement de shou ka question masu ka question desu ka question su ka question desu response ka question
query generation tools that allow users to enter kadafi and find the other possible spellings are designed into spot
this provides users with a single user interface tool to learn while providing them with a choice of search engines
in order to calculate equation NUM two kinds of information to answer the following questions are required as discourse knowledge q l what expressions in an utterance indicates a speech act type
smaller as a symbol e.g. ka masuka itadake masu ka we can interpolate the original phmsibility by using these smaller morphemes null
support multi lingual data internationalized support is fairly easy to obtain commercially for a number of commonly supported languages
for example we located a large japanese to english thesaurus that was available in electronic form
null in addition words that are of a foreign origin are often transliterated in a number of different ways
maximize performance spot was designed to be the user interface for a large archival database of hundreds of gigabytes of data
the browser also displays the original document in its native script
support multiple search engines our government users currently use a variety of tools for different purposes
means that endexpr appears next to endexprz
plausibility of coherence relations between the endexpr expressions
full text processing improving a practical nlp system based on surface information within the context
figure NUM l xaml h of translation i
modifies girl provides a clue that with a telescope in sentence NUM is likely to modify girl
set of sells tags its probability in the population is given in NUM
job of i q turing the dependency s ructurc l able NUM
however we do not expect humans to be concerned about their social standing with a machine unlike in the human interpreted setting
then ul is interrupted and utterances follow based on r18
when a more concrete domain plan is obtained an utterance plan is replanned
persuaded act a dialogue partner is persuaded to adopt domain action a
specifically candidates for speech recognition or parsing could receive higher preference scores if they include lexical items or structures previously encountered in the discourse
this model utilizes such a discourse structure to incrementally produce utterances constituting a discourse
thus what we see in these results is the natural tendency of humans to accommodate to their interlocutors in a variety of communication environments
figure NUM shows discourse relations tllat appear in the discourse displayed in figure NUM
in two other similar experiments english speaking clients interacted with japanese speaking agents both to get directions and to make hotel reservations
this dimension is introduced by discourse referents
c elle ne se laissa pas faire
while the precision is similar to that of our algorithm the coverage is NUM worse
the evaluation is done on a set of related nouns from roger s thesaurus tagged by hand
we do not make the linguistic claim that passives should be analyzed using such a lexical rule
the disjuncts in the constraint on derived word on the other hand encode the lexical rules
most current hpsg analyses of dutch german italian and french fall into that category
for each solution to a call to q l the value of is a derived lexical entry
tion of the lexical rules and the lexical entries remains without a special specification of exceptions
first both japanese and english texts are part of speech pos tagged NUM and stored in memory as in figure NUM pos tagging is required for two main reasons NUM there are no explicit word delimiters in japanese and NUM by using pos information useless expressions can be removed
our method com null prises two steps NUM extracting useful word chunks n grams by word level sorting and NUM constrncting bilingual collocations by combining the word chunks acquired at stage NUM
for example the coincident string between japan and china and japan and costa rica is japan and in our method while it is japan and c in previous methods
in fact nlore i han half of a ii lh xibh collocations acquired were like no NUM to remove useh ss cojj tlions co stra inl s
the gesture recognizer currently recognizes a total of twenty six different gestures some of which are illustrated in figure NUM
an underspecified scope domain is represented by a hole
we are actively pursuing incorporation of statistically derived heuristics and a more sophisticated dialogue model into the integration architecture
in sentence NUM a nominal anaphor occurs der t3100sx a particular notebook
first we examine the errors which all strategies have in common for the success rate cf
the results change slightly if semantic conceptual constraints type and further admissibility constraints on anaphora are considered
neither gives any preference for a particular antecedent at the sentence level nor do they consider text anaphora
note that we tried to eliminate error chaining and false positives for some remarks on evaluating discourse processing algorithms cf
an empirical evaluation indicates that the functional information structure guides the search for an antecedent within the sentence
we extend the centering model for the resolution of intia sentential anaphora and specify how to handle complex sentences
hence context bound discourse elements are generally ranked higher in the c than any other non anaphoric element
we consider such metrics to be a method which detracts from the exact linguistic specifications as we propose them
in order to empirically strenghten this argument we have examined several texts of different types NUM texts from the information technology it domain one text from the german news magazine der spiegel and the first chapters of a short story by the german writer heiner miiller NUM cf
note that an active pair n i can turn at most ilhs ri i hr active pairs into dead ones through a call to the procedure update
this is compatible with the post order application requirement
we also have that rule l lcb m16 rcb rule NUM lcb m17 rcb rule NUM NUM and h contains indices NUM and NUM
apart from the restriction to tree rewriting the main difference between nce grammars and tts s is that in the latter formalism the productions are totally ordered therefore there is no recursion
optimality theory a constraint based phonology and morphology paradigm has allowed linguists to make elegant analyses of many phenomena including infixation and reduplication
algorithm NUM should then be modified as follows
this is more precisely stated in what follows
l s inthe ot tableau in figure NUM we see that align also gives two marks to the optimal candidate
n that case although we employ a general method for iml lieationm line ar deduction the resuits we derive will i e all and only those that retie validity of the weaker system
the tagger lemmatiser currently functions as a lexical front end for a syntactic parser
the bimodality of this ratio for several values of n u i is illustrated in figure NUM
both models made some errors because of this tokenization problem albeit in different places
it is not clear how the precision recall trade off can be controlled in the ibm models
in this manner the model can account for a wider range of translation phenomena
then vk and uk l will also co occur more often than expected by chance
the purpose of the competitive linking algorithm is to help us re estimate the model parameters
a word to word model of translational equivalence can be evaluated either over types or over tokens
figure NUM pr links model has only one global max
the wrong link and missing link error categories should be self explanatory
we evaluated five random samples of NUM link types each at three levels of recall
the set of entries in a superentry can be seen as an hierarchy of a few original senses and a number of senses derived from them according to well defined rules
many lrs produce entries in which the syntactic category of the input form is changed however in our model the semantic category is preserved in many of these lrs
such lrs automatically produce word forms which are polysemous such as the spanish generador generator either the artifact or someone who generates
as with run time loading the risk is that overgeneration will cause more degradation in accuracy than the missing derived forms if the lrs were not applied in the first place
while shifts and modulations are important we find that the main significance of lrs is their promise to aid the task of massive lexical acquisition
null lexicon load time the lrs can be applied to the base lexicon at the time the lexicon is loaded into the computational system
figure NUM below shows tlle derivational morphology output for eomprar with the associated lexical rules which are later used to actually generate the entries
this paper deals with the discovery representation and use of lexical rules lrs in the process of large scale semi automatic computational lexicon acquisition
it turns out also that for a polysemous verb the adjective does not necessarily inherit all its meanings e.g. perishable again
in this example no forms rejected by the dictionaries were found in the corpora and therefore there was no reason to generate these new entries
as a result NUM NUM tuples remained including NUM NUM noun types and NUM NUM verb types
p v v lc is the probability that a randomly ex
NUM he suggestion is only correct for tumorale whereas laterale is of the type mc bodyside and expansie of the cc combi
words which have a meaning in general language are marked gen to indicate that the guess is morc questionable than in other cases
cen gives an a mlysis of a surgical procedure centered arotmd the surgical deed concept which is often a verb
the input is taken fl om a report of a ncurosurgical intervention the output is generated by the multitali system
the normaliser ensures that all parameters for a head noun sum to unity
a variety of statistical methods for noun compound anmysis are implemented and compared
figure NUM accuracy of tuned dependency and adjacency model for various training schemes
conceptual association outperforms lexical association presumably because of its ability to generalise
this is true across the whole spectrum of training schemes
the significance of the use of conceptual association deserves some mention
table NUM shows the number of each kind and an example of each
this will conceal any preference given by the parameters involving q
figure NUM accuracy using a tagged corpus for various training schemes
figure NUM accuracy and guess rates of lexical and conceptual association
the transition arc t1 between the states l and s1 is generated by the rule schema in figure NUM
note thai the query to dcp freeze NUM append ill NUM NUM is used to obtain union of indices values of daughters and the result is written to the indices values of the mother sign
for a certmn given restriction schema rs eore fs l res fs r rs and sub fs r is a minimm feature structure such that core fs r u sub fs r fs r
in tile example if we add syn counter rcb to a restriction schema and replace fs r with eorc fs r in the mgorithm for generating las the termination problenl does not occur because las can contain a loop and equivment signs are reduced to one state in las
NUM wa llster NUM NUM these systenls ha w been buill as ta sk specifi expert systelns focused oil the a l i lic d ion of tim idea s
note however that as the central system ol ject is in cha rge it must send messages or otll erwise cosltact the wu ious modalities of intera ctiou to be aware of tlte
ilher face beyond present NUM is ljreseul lcb ls axe in tegra l ion of objecl orienled comimlin NUM and cvc ldriv en prog rav
step NUM architectm e design since tlw re quired specification is tim most difl ctjt synergy lewj he a rchit ect ure is blackl oa rd
the a l sence of fusion is ca lled indel eudent whereas the l resence is referred to as coml il ed ause of modalicies expresses the tempora l
systelll develo inenl eca use the underlying principles a re differenl between preseul iui a nd n ull i nio llj syst eins
the combina tion of the verb by multimodal NUM clause a ud the secolul object NUM clause is m exami le of the level NUM exl ressions lit the le vel NUM sect iou
its function is to integrate different sometimes conflicting assertions that com e from different sources
james status unk post chairman org mccann erickson holder robert l
if the trigger is passive the in person is the object of by
the second component matches an empty string or one of lcb hundred thousand million billion trillion rcb
incorrectly treating john dooner will succeed james as a name caused NUM te error
pie had NUM recall and NUM precision for the walkthrough article in the ne test
a subtree pattern matcher which works on any tree structure not just dependency trees
a spurious out event dooner out as president was generated because of NUM
heuristics that are inspired by the centering theory NUM NUM
we demonstrated that a broad coverage parser can be very useful in information extraction
i ohsaka l ohsaka NUM l roposes a similar approach in which pragmatic constraints are used to determine rethrents of zero pronouns
while we call see several differences of use the most remarkable one is the difference of use of the matrix clause
the zero pronoun ca does not refer to tim hearer the user even though qs refers to the user as well a s NUM
the intuition of native sl eal ers of japanesc for NUM is that cd refers to a machine or a certain part of the machine
the subordinate clause aud the matrix clause conjoined by one of these particles correspond to the antecedent and the consequence respectively
even if the conjunctive reba in the sentence NUM is changed for tara or nara the sentences are still acceptable
note that to create fable NUM NUM and NUM several japanese native speakers determine referents of zero subjects according to contexts
in order to ascertain our estimation we have examined a bunch of real sentences which appear in real instruction manuals
note tha t tile ontology in this paper does not refer to all of objects in the world described by manuals like a certain part of machine
most of pragmatic information and colnrnousense knowledge are not used here because the result of these knowledge would be overridden by some other information such as contextual intbrmation
the systemic functional framework provides us with representational means for describing available choices and for mapping even though indirectly communicative goals to intonational features
as a starting point we have taken two existing NUM systems the cor dialogue model and the komet penman generation system
the interpersonal part of the grammar provides the speaker with resources for interacting with the listener for exchanging information goods and services etc
based on this we determine the kinds of linguistic and pragmatic knowledge needed to sufficiently constrain choice in the intonational resources of the system
this is not solely a computational requirement but is necessary for the grammar to be well defined overparsing cycles must be harmonically suboptimal otherwise arbitrary amounts of overparsing will be permitted in optimal descriptions
however NUM NUM does exist but wilhout discount
the closesl other red departure is at NUM NUM
kroatischen teil bosniens or in der moslemisch kroatischen fsderation
let NUM be the vocabulary of all words seen in training data t be the set of all part of speech tags and ttcaza f be the training set a set of reduced sentences
this paper describes a new parser which is much simpler than spatter yet performs at least as well when trained and tested on the same wall street journal data
to state that the jth word in the reduced sentence is a modifier to the hjth word with relationship rj NUM af stands for arrow from
english is largely right branching and head initial which leads to a large proportion of dependencies being between adjacent words NUM table NUM shows just how local most dependencies are
additional context in particular the relative order of the two words and the distance between them will also strongly influence the likelihood of one word modifying the other
the parse tree in training data indicates a relationship in only one of these cases so this sentence would contribute an estimate of NUM NUM that the two words are related
given s and b the reduced sentence is defined as the subsequence of s which is formed by removing punctuation and reducing all basenps to their head word alone
i would like to thank mitch marcus jason eisner dan melamed and adwait ratnaparkhi for many useful discussions and for comments on earlier versions of this paper
estimates NUM and NUM compete for a given pair of words in test data both estimates may exist and they are equally specific to the test case example
we call these output structures text items
suppose available to us are frequency data cooccurrence data between verbs and their case slot
that is the number of parame algorithm clustering NUM divide the noun set n into two subs0ts
lpar k often referred to as the parallleter description length is calculated by
in the reflexive case of linking for completion b and b are unified with each other
this is linguistically plausible in the following example where both np and det are indirectly left recursive
it can be used either to compile a given cf grammar into a parser or to interpret it
to parse a string the current word form is first parsed i.e. looked up in the lexicon
accusative plural for n genitive singular weak inflection for a and first person plural present indicative for v
open lexical link nt cat new word wor cat rcb
therefore it is legal to infer a class of type t when the discourse introduces art individual of type
of course the extensional objects arc lmke also y their structural and functives subobje ts
leaf cat nt word setof c lex word c cs cs member old cs link nt old
like in an hmm we take into account initial probabilities r transition probabilities a and class i.e.
the motivation to derive these fsts from hmms is that hmms can be trained and converted with little manual effort
NUM det which occurs unmarked as c e.g.
for example NUM NUM and NUM in figure NUM couhl be imt erative sentences in certain contexts
the eoml arison of these two structures indicates that the new intbrmation x tom is the scope of also in sentence NUM
we described and experimentally evaluated a system feaspar that learns parsing spontaneous speech
we describe and experimentally evaluate a system feaspar that learns parsing spontaneous speech
for esst the lexicon contains domain independent syntactic and domain dependent semantic microfcatures
the number and selection of microfeatures are domain dependent and must be made manually
br clarity this example assumes that all networks perform perfectly
it generalizes well and is robust towards spontaneous effects and speech recognition errors
this parser requires only minor hand labeling and learns the parsing task itself
and carnegie mellon university usa lcb finndag waibel rcb ira
a feature structure is a set of none one or several feature pairs
argmaxp2 ci tik a tlkltp eoio b ciltik NUM
the roots of the parse trees are specified by top level frames
as feature structures underspecified feature structures can encode partial information
the least specific type is at the bottom of the tree
for the unification operation and the subsumption relation on feature structures
a constant is given by any type from the type hierarchy
now the disambiguation rule explained above will initiate a clarification dialogue to disambiguate the object
in this manner feedback can be conveyed to the user without explicitly asking a questions
beverly hills by contrast has low positive scores both for place and for person
these processess form part of the talent tool set under development at the t
finally nominator groups together name vari null ants that refer to the same entity
it is not possible to determine relative scope strength for all the combinations of different operators
some heuristics weigh the relative scope strength in the substrings on either side of the operator
however if they are incompatible with any they are assigned a weak entity type
in terms of world knowledge the most obvious resource is a database of known names
in general two types of resources are available for disambiguation context and world knowledge
all of these ambiguities must be dealt with if proper names are to be identified correctly
there are two ways to determine the extent of the left and right environment one is to specify a fixed number of characters and the other is to use a look up and match procedure to identify specific morphenms
therefore if movement and conjunction is of single constituents phrase structures a d explain this evidence but e h do not
d she7 often beats her b
although this research has been the most successful of all approa es it is difficult to see what use could be made of the word sense distinctions produced
the sentence is washiriki wa semina zote walitoka katika nchi za afrika
if the target is left without parentheses it is interpreted as a set
context conditions is an optional part but in most cases necessary
syntactic parsing is carried out with rules located under the heading mappings
the improvement is not significant however
a probabilistic disambiguation method based on psycholinguistic principles
this function has the disadvantage that longer definitions are prefered over short ones since these simply have more words which can contribute to the overlap
that was one more reason she did n t look forward to cathy s visit short or long as for this rider i never saw him before or afterwards and never saw him dismounted so whether he stood tall or short in his shoes i ca n t say skin colors range from white to dark brown heights from short to tall hair from long and straight to short and tightly curled
this insight has particular relevance or the apparent puzzle presented by sloppy identity and related phenomena
partof speech tags can provide a valuable step towards the solution to sense tagging fully disambiguating about NUM of ambiguous word tokens and reducing the ambiguity for some of the rest
the desired reading can now be derived as follows NUM a smith spent his paycheck
to determine the importance of a sentence several superficial features are considered and weights for features are determined by multiple regression analysis of a hand processed corpus
this paper proposed a method for this adjustment that is a method for determining weights of surface features by multiple regression analysis of abstracts created by human
the rhetorical relation is determined by checking special expressions both in the first phrase and in the last phrase of a sentence
therefore another strategy is needed for languages in which a predicative phrase may be located in the middle of a sentence
distance from the end of a text sentences located near the ending of a text also tend to be important
this paper describes a system which automatically creates an abstract of a newspaper article by selecting important sentences of a given text
little notice has been taken of punctuation in the field of natural language processing chiefly due to the lack of any coherent theory on which to base implementations
the possibility exists that this rule may apply to further categories such as adjeel iwd and adverbial although instances of this were not found in the corpus
we have seen that by extracting punctuation patterns from a corpus it has been possible to postulate a small number of generalisations for punctuation rules within nl grammars
spoke specifically of a third way of having produced a historic synthesis of socialism and capitalism
ni np np the only instance in the whole corpus of this pattern was a book title NUM
punctuation does not seem to occur at levels below the phrasal with one exception punctuation is allowed to occur at any level in the context of coordination
conjunctive punctuation use can bc seen in NUM where although occurring below the level of np the pnnctuation is legal because of its eonjmmtive context
our evaluation is unsatisfactory due to the small test set but does demonstrate that the use of independent knowledge sources leads to an improvement in the quality of disambignation
these rules were then redu e l to just lgz underlying rule patterns ik r the colon seinicolon dash comma full stop
therefore to acquire a vocabulary of just 20k words without using frequencies of NUM or less clearly requires a training corpus larger than NUM million words
according to krashen in order to facilitate quicker progress the most helpful input will include features of the i l level
for example in the first case the user s placement in slalom would indicate that he she has already mastered the appropriate verb morphology
by looking at language on a feature by feature basis we have identified several language mis matches that may lead to negative transfer
this could explain omissions of the morphological marking s on these verbs in the written english of proficient asl signers
we have depicted parts of four hierarchies in the figure morphological syntactic features noun phrases verb complements and various relative clauses
if the user model is updated and the user s placement on slalom changes a new ordering of the alternatives would reflect this change
NUM while in theory instruction in asl would be useful the generation of asl is well beyond the scope of this work
the mal rules expand the coverage of the grammar to include the errors that might be expected from people acquiring written english as a second language
once the errors have been identified the system must decide which of these errors it should concentrate on in its response to the student
this is estimated from the relative frequency of a verb co occurring with any noun in class c NUM namely
for a stacked relation i j NUM NUM t NUM otherwise
the dispatched activities represents therefore the priming power of the priming lexemes o51 each microselnantic case
the following experimental results will thus suggest the suitability of our mnodcl for spontaneous speech parsing
the dispatching is thus restricted to the recalled relations every time a
the two parse trees are indirectly related by an anaphoric a lation ref
as a result the microsemantic parser can not unify the main clause and the comment
although it favors the most recent lexemes this process does not prevent long distance primings
closed questions are consequently characterized either by a prosodic analysis or by the adverbial phrase est ce que
any speech recognition system involves a high perplexity which requires the definition of topdown parsing constraints
parsing spontaneous speech is a difficult task because of the ungrammatical nature of most spoken utterances
this paper was about the following aspects of ling null ware development e linguistic felicity and leanness
optimal distribution of inforlnation over analysis and refinement results in a gain of efficiency by several factors of magnitude
the th component represents a preprocessing component which covers a substantial part of what occurs in real texts
the lingware created is es pecially interesting ill that it provides an integrated design for all the modules
the mother of such a construction also comes out as npacc which is encoded in projects
the feature type bears the variable type in our case quantities
the difference to the head comp schema is that head information comes from the functor also the semantics
qn as complement l subj the content of a pp is a relational psoa
description has to be done in great tail all frames all syntactic realizations of frames
applications on the basis of unification based grammars ug so far are rather rare to say the least
the numbering of the facts corresponds to the order in which they are derived
magic evaluation constitutes an interesting combination of the advantages of top down and bottom up evaluation
this allows it to be optimized in a processor independent and logically clean fashion
as a result of the definite clause characterization of filtering magic brings filtering into the logic underlying the grammar
NUM det aip p nsem a nsem
the seed is identical to the one used for the example in the previous section
let p be a program and q e a query on the program
at the same time it often remains unclear how these optimizations relate to each other and what they actually mean
two filter optimizations based on the program transformation technique of unfolding are discussed which are of practical and theoretical interest
one such approach involves augmenting a base model built from a larger more general corpus with information from a small sample of the domain specific language
predict i returns a set of currently applicable cfg rules at position i
chart parsing proceeds using the following two procedures
many plan inference models have thus been proposed
l rocedure NUM let ei be an inactive edge
the paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history thus enabling the use of long distance dependencies
if it is possible to identify which clauses are central to a text the information can be used to contribute to a relevance assessment or as the basis for a derived summary
once the topic and focus are determined the remainder of the semantic representation is assigned as the gronnd
ltowever verbs are usually not in focus unless they are surprising or contrastive or in a discourse initim context
the noncanonical osv word order in NUM b is contextually appropriate because the object t ronoun is a discourse old topic that links the se ntence to the previous context and the sul jeet your father is a discourse new focus that is being contrasted with other relatives
a comparison of the NUM discourses in the first two rows NUM of the tables in figure NUM using the chisquare test shows that the association between sentence position and cb is statistically significant x NUM NUM NUM
when we translate an english text into a free word order language we are faced with a choice between many different word orders that are all syntactically grammatical but are not all felicitous or contextually appropriate
in turkish discourse old information that is not the topic or focus can be NUM a dropped b postposed to the right of the verb c or placed unstressed between the topic and the focus
once the im formation structure for a semantic representation is constructed using these algorithms the sentence with the contextually appropriate word order is generated in the target language using multiset ccg a grammar which integrates syntax and information structure
the summaries will be compared against summaries of the same texts compiled by the syntactic technique mentioned previously and also against summaries consisting of the first paragraph of each news article
this section presents a framework for describing infixalion rules using our multi tape two level formalism
model NUM assigns probability to different binary parses of the word k prefix by chaining the elementary operations described above
l eduction in implicational linear logic lacks both of these features although as we shall see shortly some notion of span can be specified
we will instead use a special variant of substition which specifically does not act to avoid accidental binding notated e.g.
the oml iladon t rocess will return a full set NUM of l he mfi lue iudices assigmxl to any brnntlae
the method to be described involves compiling tile initial formulae which may be higher order to give a new possibly larger set of formulae which arc all tirst order
the possibility arises that compilation inight insert tile absl rael ion into the semantics of the compiled tbrmula so that it latex binds the variable of the additional formula
given a possible theorem br bn a tire left hand side formulae are each assigned unique indices and semantic variables and t ul on ail agenda
standard chart parsing for psg has the adwmtage that a simple organising principle governs the storage of results and underpins search namely span within a linear dimension specified by limiting left and right points
lo labeled inq li ational linem l yl es wil h deduct ion implement ed via a version of si d resolution
met hod lcb hat converl s form thin lo a form for which a chart like deduction me lcb hod is t o qsi ble
figure NUM before an adjoin operation h z h NUM h o
placed around the upper side of a replacement expression that presupposes a strict alternation of empty substrings and non empty substrings of exactly one symbol e x e y e z e NUM in applying this to the above example we obtain
even if such corpora are subdivided to a further level of classification they still suffer the same problem albeit at a finer level of detail
the ratio of these probabilities is the likelihood ratio in favor of u and v being mutual translations for all u and v link frequencies generated by the competitive linking algorithm
in practice our linking algorithm can be implemented so that its worst case running time is o lm where l and m are the lengths of the aligned segments
if uk and vk are indeed mutual translations then their tendency to zthe co occurrence frequency of a word type pair is simply the number of times the pair co occurs in the corpus
the ed constant refers to a resulting state e.g. adorned for the verb adorn
this transition ordering is used for ranking discourse entities in the order of preference as the antecedent of a zero pronoun
we tilink it is inadequate since the antecedents of zero pronouns often appear in the previous pre partitioned sentence
l meuts nakaiwa a n l kehara NUM nakaga wa
if the original centering algorithm is applied to each sentence uniformly the counter intuitive interpretation is obtained as mentioned above
tile experiment shows that the accnraey improves until the previous NUM NUM sentences are searched but degrades after that
zero NUM l ollolllt r sohti iolt lit sectiou rcb lro wo
in the last section we des ribed our zero pronoun resolution method that can handle colnplex sen
totally taking into account these results we determine that tile antecedents are searched in the previous four simple sentences
NUM road cov rag z ro l ronoun r solution system
information we think they are dif icult lcb o be tune NUM to th
these representations have been ported into english arabic and spanish lexicons each containing approximately NUM verbs
1we focus on building entries for verbs however we have approximately NUM NUM non verb entries per language
NUM NUM experiment NUM improvement of stochastic tagging
NUM NUM environment of a string in a corpus
therefore we obtain the following formula
the results of word extraction experiments attested the correctness of our hypothesis
table NUM environment of the string NUM
using this expanded dictionary the tagger s accuracy improved to NUM NUM
unknown words are inevitable at any step of analysis in natural language processing
table NUM examples of extracted words from science
word extraction from corpora and its part of speech estimation using distributional analysis
introducing these intermediate levels into the statistical framework gives
this process yields a set of candidate input vectors x
in this paper we present such a system
the selection of atis was motivated by three concerns
is equivalent to finding mo that maximizes
y represents the post discourse meaning mo
probability along the path corresponding to t
word transition probabilities have the form
figure NUM overview of statistical processing
and thus the former interpretation was mistakenly preferred
there is no need for synchronization between conlponents during the configuration of the con munication system
the ils maintains a mapping fl om compolmnt names to those task ids
it also records the actual configuration of the modules present in the application and the created channels
if a word can not be used in any way it is simply rejected
at each of these points a set of word hypotheses is sent to the parser
all communication is clone by the trieatls of channels as set out above
the actions performed by the ils include attachment and detachment of components
the implementor need not track those configuration messages the communication layer handles this transparently
the notification consists of the necessary data to create the intended channel within the component
moreover we expect that dm methodology we used can be applied to more expressive discourse rel resentadon hmguages in a similar way
but we must of course admit l hat further study is needed in order to be able to determine these conversions
in contrast to knowledge retrieval where incompleteness is assumed for utility reasons inference systems used for lexieal disambiguation have to be essentially incomplete
semantic representations of natural language texts in this language do in general not satisfy conditions which make them de idable see e.g.
we can only find representations whose models include the models of NUM but not a representation with exactly the same models
if the discourse were continued as in NUM NUM einige arzte haben eine schwester mit der sic verheiratet sind
as the analysis of the examples in this paper has shown there is a striking similarity between lexical disambiguation and anaphoric resohltion
moreover an additional test based on the procedure sketched in footnote NUM would certainly exclude the sister reading for laa
thus we have to assume iv lexical disambiguation is very often possible although the discourse contradicts our conceptual knowledge
it was demonstrated that the resulting abstracts have the same quality in terms of precision recall as the abstracts created by human subjects ill an experiment
our algorithm compares favorably to standard bottom up parsing methods for scfgs in that it works efficiently on sparse grammars by making use of earley s top down control structure
that is if the same state is generated multiple times the previous probability associated with it has to be incremented by the new contribution just computed
because of the possible start indices each state set can contain NUM l earley states giving o NUM worst case space complexity overall
the crucial step in this procedure is the addition of variants of the original productions that simulate the null productions by deleting the corresponding nonterminals from the rhs
the outer probabilities are computed by tracing the complete paths from the final state to the start state in a single backward pass over the earley chart
let c x a x denote the expected number of uses of production x a in the derivation of string x
the parser keeps a set of states for each position in the input describing all pending derivations a these state sets together form the earley chart
the inner probabilities on the other hand represent the probability of generating a substring of the input from a given nonterminal using a particular production
these restrictions ensure that all nonterminals define probability measures over strings i.e. p x x is a proper distribution over x for all x
however whereas their focus is on the evaluation of abstracl readability as stand alone texts ours is rather on abstract relevance
finally the system yields significantly better results than a default lead algorithm does which chooses just some initial sentences from the text
towards translating spoken language pragmatics in an analogical framework
NUM we have omitted the nature s selection among the semantic contents
also the nodes with the same label are collapsed to one
a general conjecture we might draw from this discussion is the following
NUM natural language meaning games are played at their pareto optimal equilibria
extralinguistic context enters sentence level games and plays an important role in language use
in natural language communication a meaning game can influence the next meaning game
this common knowledge about m may be even incorporated in the meaning game
though isamai was designed for general purposes the method alh ws it to reflect a specific domain through tile use of a dt ma in dependent
we will discuss this feature in section NUM NUM
figure NUM the simplified feature structure for the
dependencies among the levels are expressed by coreferences
this requirement also constrains the exchange of messages
finally some experimental results will be reported
much of semantic descriptions are of this kind
this work was funded by the german federal ministry of education science research and technology bmbf in the framework of the verbmobil project under grant NUM iv NUM k NUM
in contrast to this our approach allows to exploit tim fact that the grammars employed by the parsers are derived fl om the same grammar and thereby similar in structure
nevertheless the overall time is still only NUM of the system with the complete gramifiar a sequential coupling only improves the overall run time for semna only by NUM NUM
the parsing time of the coupled system is slightly higher than that for syn parser alone due to the fact that the sem parser can only terminate after the syn parser has sent its last hypothesis
table NUM results with voting constraints
tion labels by an appropriate amount
we have applied our approach to disambiguating turkish text
the weights for these rules are currently manually determined
figure NUM sentence as a finite state recognizer
all parses with votes equal to or higher than vt m vh vt are selected with m NUM m NUM being a parameter
for the purposes of this work the vote of a rule is determined by its static properties but it is certainly conceivable that votes can be assigned or learned by using statistics from disambiguated corpora
in all languages words are usually ambiguous in their parts of speech or other morphological features and may represent lexical items of different syntactic categories or morphological structures depending on the syntactic and semantic context
therefore we ignored english words that were linked to nothing
whether incomplete links are correct or incorrect depends on the application
this is estimated from the relative frequency of v co occurring with the nouns in word class c namely
an efficient probabilistic context free parsing algorithm that computes prefix probabilities
formal definitions of these conditions are given in appendix a
we will refer to them collectively as dummy states
the problem is best explained by studying an example
it can be computed as the product
then given va the association score a ce fal v s of cg and fj is defined as
in this paper we apply its idea to the sense classification of japanese verbal polysemy in case frame acquisition from japanese english parallel corpora
we compared the result with the hand classification and checked whether each cluster contained examples of only one hand classitied sense table NUM
this work is partly supported by the grants from the ministry of education science and culture japan NUM
this sequence is constructed by taking into account the total order on the labels at every level that is 17i is lexico qraphically
seen that the approximate search algorithm is very fast for the set of synthetic tree d tabases that we have experimented with
the rules are implemented as prolog clauses which guarantees a declarative style of the rules at east to a large extent
and b is presented in NUM a
russian sebe is a subject oriented refle xive
maria talked to himself about pedro b
by the way the construction of d solve the emptiness problem for ligs l specify the empty set iff the set vt d is empty NUM
of course we are ti ee not to mention the additional piece of information but this is not really the point
assume that the symbol a b is useless in the ldg dl associated with the initial lig l we know that any non terminal s t
moreover during an extraction since dl is not ambiguous at some place the choice of another a production will result in a different linear derivation
only good trees are kept NUM the lig constraints are ire checked while the extraction of valid trees is performed
its liged forest whose start the start symbol of the ldg associated with l x is a
g z is its cf backbone and its productions are the productions o p in which the corresponding stack schemas o l have been added
although these sentences are intended to test a system s ability to translate one basic linguistic phenomenon in each simple sentence the result was strong evidence for our claim
development of an efficient translation algorithm not just an efficient parsing algorithm will make a significant contribution to research on synchronized grammars including stags and our pcfgs
the parsing algorithm for translation patterns can be any of known cfg parsing algorithms including cky and earley algorithms 1deg at this stage head and link constraints are ignored
for a cfg g the set of its rightmost s xderivations where x e e g can itself be defined by a grammar
with the explosive growth of the world wide web www as information source it has become routine for internet users to access textual data written in foreign languages
another word change the underlying meaning replan figure NUM the lexicon i l as mediator between the
unification of binary features however is much simpler unification of a feature value pair succeeds only when the pair is either NUM NUM or NUM NUM
construction of the target charts if possible on the basis of the m best candidate patterns for each source chart takes o kn m time
this information can be conveyed through various forms of evidential markers and devices to express the speaker s certainty uncertainty or the speaker s perspective
the obvious problem as with neural nets is that the system s knowledge is not easily expressible as a set of human readable rules
in the remainder of this paper i shall concentrate precisely on this latter problem by showing the interaction between these two components
second the years are treated as a nominal variable instead of an ordinal one and thus sorted according to the profit values
lbkenization sentence splitting and spelt checking are carried out according to rule by the treebankers themselves see NUM NUM above
these collocations are at the same time substrings of other longer collocations
let us recall that collocations are domaindependent
are the other three extracted as well
collocations lhat are subst rings of olher longer ones
table NUM gives the extracted by cost criteria n grams containing wall street
the above algorithln coinputes the c value of each string in an incremental way
it is the particular syntactified combinations of words that reveal this structure
this is tile reason we consider tile method as semi automatic
itowever a should not be extracted as a collocation
other researchers also have investigated methods for adapting large general lms using data from a small domain corpus and have found merit in simply building a smaller lm directly from the domain corpus
firstly the lm is being used as the representation of a training text against which similarity is to be judged and yet it is by definition undertrained and therefore degenerate
consequently the best way to acquire more ernail data appears to be either a to instigate a further collection initiative b to use more sophisticated bottom up
the british national corpus bnc is a suitable example since it contains NUM million words of modem english both spoken and written sampled from the widest range of materials
the issue here is essentially one of quantity since it is shown that effective language models can be built from very modestly sized corpora providing the training data matches the target appfication
moreover each rightmost s x derivation in g is the reverse of a sentence in e d
but such a method is not practical since the number of parse trees can be unbounded when the cf backbone is cyclic
we will show that the analogous holds for ligs and leads to an o n NUM time parsing algorithm
this means that the average parsing time is much better than this NUM n NUM worst case
at first sight there are two conceivable ways to extend this recognizer into a parser NUM
in this section we will see how to build a cfg which represents all parses in a lig
afterwards we build the reduced ldg dl associated with l as shown in section NUM
so at the end the number of productions of that form is o nh
tile dots in the subhierarchies represent the senses of eilhcr the word to be disambiguated w or the words in the context
figure NUM while br b20 shows clear improvement for bigger window sizes br r05 gets a local maximum at a NUM size window etc
besides completely disambiguating a word or failing to do so in some cases the disambiguation algorithm returns several possible senses for a word
when no further disambiguation is possible the senses left for w are processed and the result is presented step NUM
first the algorithm represents in a lattice the nouns present in the window their senses and hypernyms step NUM
the frequency counts for each sense were collected using the rest of semcor and then applied to the our texts
if this was the case the error rate could be furtber decreased setting a ccrtain lhreshold for conceptual density wdues of wilming senses
text words nouns nouns monosemous an average of NUM of all nouns in these four texts were not found in wordnet
the NUM NUM tries to smooth the exponential i as m ranges between i and tim total number of senses in wordnet
l he expression the b zs goes can be divided into two constituents the bud and goes
the passive arc NUM which is relevant to goes to chinatown at ten a m h two competing rcsuits
1there are other types of patterns such as x a y fl where ce and NUM are constituent boundaries
the assignment of the functional word a m to the pattern x a creates a passive arc by combining another passive arc
a solid line denotes a passive arc that covers a substring of the input below while a dotted line denotes an active arc
many of these techniques adopt a left to right strategy to handle an input incrementally and a best first strategy to avoid the explosion of structural ambiguity
this paper proposes tdmt using an incremental strategy for achieving efficient translation of a lengthy input or one having a lot of structural ambiguity
for instance the expression goes to chinatown is divided into two constituents i.e. goes and chinalown
in conventional chart parsing many arcs can be created because every word can create active and passive arcs based on its part of speech
thus the combination of NUM and NUM is selected as the structure of the passive arc NUM
our method not only extracts fixed collocations with high precision but also reduces the combinatorial explosion that would be otherwise considered inescapable in extracting flexible collocations
all hough coltventionaj nlelllotis focus on houri llhrases or ry t o en onll ass all kinds of olloca tions
a path is said to be constrained by or to generate a string x if the terminals immediately to the left of the dot in all scanned states in sequence form the string x
in the field of machitm translation there is a growing interest in corl lls i ased al l roa hes
associated with the maximum is recorded as the viterbi path predecessor of kx y the other predecessor state kx y can be inferred
as was the case for forward probabilities fl is actually an expectation of the number of such states in the path as unit production cycles can result in multiple occurrences for a single state
computational linguistics volume NUM number NUM an essential idea in the probabilistic formulation of earley s algorithm is the collapsing of recursive predictions and unit completion chains replacing both with lookups in precomputed matrices
the restriction in a that x be preceded by a possible prefix is necessary since the earley parser at position i will only pursue derivations that are consistent with the input up to position i
partial parses are assembled just as in nonprobabilistic parsing modulo possible pruning based on probabilities while substring probabilities also known as inside probabilities can be computed in a straightforward way
there is a one to one mapping between partial derivations and earley paths such that each production x applied in a derivation corresponds to a predicted earley state x l
the spurious ambiguity that we have just identified is particularly pernicious since it would aid el a wide range of sentences in any grammar of german that employs argument composition and the jei i lcb g all assertion main clauses that contmn auxiliaries would be affected since in assertion clauses the initim constituent is the result of topicalization
in particular the comps list of the pplr is entirely schematic r r any non empty list of categories whose tlrst element is an accusative n p while the comps vahle for kaufi n is a list with exactly one element that has the same category and case spc cification as the np in the piq r
the prol oscd consl itueih slrllcl urc l cquircs that sul ca tcgorizal io l in for w lion m out non wm al coml h m0 nts is l rol agal ed from the main verb to the l op of the vcrlml comf lcx
one of the possible outputs of the i3 lcb to such an auxiliary entry wouht look identical to l he putative output of the pplit shown in fig NUM expect that one of the elements from the comps list of the auxiliary is assigned to the slash set instead of the subj list
performance measure NUM is the feature accuracy where all features of a parser nmde feature structure are compared with feature of the correct handmodeled feature structure
in this paper we describe an application oriented system that corrects such errors
a more indulgent strategy is also possible which only requires that the value of c for new sentences should be less than the initial value and not necessarily equal to NUM
this work was supported by 1ntas grant NUM NUM
it shouhl be noted dmt in contrast to certain other systems for example jensen et al NUM weischedel and sondheimer NUM v onis NUM chanod et at
results for distorted sentences which are not correct or quasi correct
the NUM initial sentences gave the following resuits
experiments with real sentences have shown promising results
for example in applications in speech recognition the optimal result has the largest plausibility value among several candidates obtained from a module of speech pattern recognition
to improve our method we will use the coherence relations in the verbs and set of nouns and will use a larger corpus with local cohesion
secondly we chose NUM dialogues from the atr database and annotated them with local cohesion by hand code such as that shown in figure NUM in section NUM
our method has obtained a NUM accuracy for closed data and a NUM accuracy for open data in recognizing a pair of utterances with local cohesion
global cohesion is a top down structured context and is based on a hierarchy of topics led by domain e.g. hotel reservation or flight cancellation
p is the relative frequency of two utterances with local cohesion and p is that of two utterances without local cohesion
using the NUM dialogues the endexpr bigrams were produced in the following four parts an endexpr bigram part a with local cohesion turn taking
the former method must use the n xm parameters i.e. u and the latter one must produce the iq speech act types
these speech act types are automatically constructed by clustering endexprs based on endexpr bigrams and then the type bigrams are re calculated from the endexpr bigrams
test sentences with unknown words and unknown category words with only unknown category words all test sentences the table shows that dop3 has better performance than dop2 in all respects cf
to increase the reliability of our results we performed experiments with NUM different random divisions of atis into training sets of NUM and test sets of NUM trees
it is one of the most essential features of the dop approach that arbitrarily large subtrees are taken into consideration to estimate the probability of a parse
ratnaparkhi NUM a single inconsistency in a test set tree will very likely yield a zero percent parse accuracy for the particular test set sentence
how can we estimate the probability of a subtree which appears as a result of the mismatch method in the parse forest but not in the training set
then the steps to be perfi rmed to achieve this goal are given
these rmmr models integrate task information into the information already available in interface models
w e address the second by providing more general text generation facilities whic h
wc have shown how existing resources can be adapted to new applications therel y saving eon siderably on develol ment efforts
i t e project u st lrm NUM NUM and funded by the austrian t brschungsfdrderungsfonds dcr lcwcrblichcn wirtsch grant NUM NUM
realize head and arguments pattern args head dtr figure d llead driven genera lion in fiji
note that these effects can be traced and undone in the case of backtracking
this paper presents a unified theory of verbal irony tbr developing a computational model of irony
this paper is organized as follows section NUM discusses the problems of previous irony theories
table NUM allusion of ironic utterances in figure NUM conditions for allusion utterances satisfying the condition
a a allusion we give a formal de finition of allusio l in our theory
i m really satisfied to at the pizza
figure NUM representation of ironic environments for examples i and NUM
ironic environment can be classified into the following tbur types
hflbns and actions call include paralnetexs denoted by capital letters
situations are partially ordered by the part of relation denotexl by NUM
table NUM pragmatic insincerity of ironic utterances in figure NUM
in the presence of models with varying complexity mle tends to overfit the data and output a model that is too complex and tailored to fit the specifics of the input data
show t hal il is i ot t er to enq loy mi i than m NUM NUM as estimation riwrion in hierarchical word chtstering
word based mle thesaurus and mdl thesaurus respectively stand tbr using word based estimates using a thesaurus constructed by employing mle and using a thesaurus constructed by our method
outperformed that of word based while basically maintaining high accuracy though it drops somewhat indicating that using an automatically constructed thesaurus can improve disambiguation results in terms of coverage
on l he ot ier hand i oth NUM an l j were conlpetitivc with t NUM oth0r ulodcis i l tagging
fhe probability of drawing a given parsed sen once froln the population may then be expressed 2this correspouds to l mi erty el al s central st ttistk p
for ex unl h lh e price of lh c sleek fell l igure 3a will tyl ically NUM e nlisanalyzed under this model
probabilistic behavior depends on il he adword he lcxicmisl hypoijmsis titan dilt erent ly hc uhxl a na lyses need dilt ereni sigmrtures
file sohttion is to nlodi y t slightly further conditioning l lj on the number and or type of children of i that already sit between i and j
pr link presences and absences i words tags NUM i i t om i NUM twom i NUM
observe that each state of ac simultaneously carries over the recognition of several suffixes of trees in lhs
what follows is standard terminology from the tree pattern matching literature with the simplification that we do not use variable terms
informally a dta m walks through a tree t by visiting its nodes in post order one node at a time
NUM assume that q e f is reached by ag upon reading a node n in some tree
if rule ri must be applied first at n n is added to rule i and h is updated
the first set contains all the nodes n at which the matching of lhs r overlaps with a second matching at a node n dominated by n
for example without reference information e3 in figure l a could mean any of a farmer beats a donkey all farmers beat a donkey all farmers beat a the same donkey or all farmers beat all donkeys
the optimal overparsing structure for category x is denoted with ix NUM and such an entity is referred to as a base overparsing structure
this is a consequence of the fact that in general parsing operations filling such a cell must consider all ways of dividing the input subsequence into two pieces
the potential confusion stems from the fact that an underparsed segment is part of the description but is not a proper constituent of the tree
a cell is identified by three indices and denoted with square brackets e.g. x a c
the resulting algorithm has a time complexity cubic in the length of the input and is applicable to grammars with universal constraints that exhibit context free locality
in the ot case presented here the full grammar is the entire ot system of which the position structure grammar is only a part
for each substring length there is a collection of rows one for each category which will collectively be referred to as a level
the decision at the left daughter of the root node concerns whether or not the segment to the right is an alveolar
the phonetic realizations and their weights were identical for both methods thus verifying the correctness of the compilation algorithm described here
the compiled transducer corresponding to that rule will replace c with c with the appropriate weights in the context a p
finitestate machines provide a mathematically wellunderstood computational framework for representing a wide variety of information both in nlp and speech processing
also given in table NUM are the compilation times for the individual trees on a silicon graphics r4400 machine running at NUM mhz with NUM mbytes of memory
the first question that is asked concerns the number of segments including the aa itself that occur to the left of the aa in the word in which aa occurs
the demo will feature aspects of the system currently being used to develop a coreference resolution engine in preparation for muc NUM in addition to an information extraction task done over the summer of NUM
note that while regular relations are not generally closed under intersection the subset of same length or more strictly speaking lengthpreserving relations is closed see below
department of computer and information science and
eagle an extensible architecture for general linguistic engineering
thus the transducers compiled for the rules at nodes NUM and NUM are intersected together along with the rules for all the other leaf nodes
in particular they specify a two level mapping from a set of input symbols phonemes to a set of output symbols allophones
figure NUM result of merging states NUM and NUM of figure NUM
given a concept c at the top of a sulfifierarchy and given nhyp mean number of hyponyms per node the conceptual density for c when its subhierarchy contains a number m nmrks of senses of the words to disambiguate is given by the ormula below
step step NUM step NUM step NUM step NUM t r ee compute tree words in window loop tree compute conc ptua distanco tree concept se occt
for comparison we altered our algorithm to also make random choices when unable to choose a single sense
the fact that some senses were discarded because the human judged them not reliable makes comparison even more difficult
the depth in the hierarchy concepts in a deeper part of the hierarchy should be ranked closer
the results of the experiments have been automatically evaluated against semcor the sense tagged version of the brown corpus
most of those works focus in a selected set of a few words generally with a couple of senses of very different meaning coarse grained distinctions and for which their algorithm could gather enough evidence
thus a sloppy reading is made possible when there is a center shift
our account for this parallels the account of sloppy identity in vp ellipsis
that is the interpretation of yp switches from controller c1 to c2
it is this change in context that gives rise to the sloppy reading
kiss youa even if youa do4 not
table NUM accuracy speed size and creation time of some hmm transducers
the result is equivalent to the one obtained by tagging the sentence as a whole
an hmm can be identically represented by a weighted fst in a straightforward way
NUM sent the tagger stops reading words from the input
adapted to a 2nd order hmm this algorithm would give an n2 type approximation
NUM lexicalise xl and link the resulting lexemic structure to v by means era deep syntactic relation i
finally the conclusion will describe some lexical divergences which may require the introduction of language specific semantic representations
in computational linguistics it has been primarily used as a theoretical basis for language generation models e.g.
the french version results from a one to one mapping between concepts of the input representation and lexemes
however it is not always sufficient in order to reach the appropriate specificity level required for the instruction
to be consistent with the lexical preferences observed in the corpus this rule should have the highest priority
however this characterisation is not always straightforward and it appears that more precise oppositions should be introduced
such constructions raise an interesting problem for mt because they can not be translated in a purely compositional manner
we can hardly get an acceptable english translation if we want to preserve the structure of the french instruction
input NUM l ag th goal park template
dex and then determines the language specifc annotations to instantiate for that template
however if a preposition occurs in the grid the marker
along swe have defined approximately NUM such mappings per language
the lexical verification process took only two weeks by the native speakers
for presentational purposes the remainder of this section uses english examples
c onsider the construction of a lexical entry for the verb adorn
the lc s produced by our program for this verb is
the variable positions for ag and th are marked
manner again is specified as an additional semantic coin ponent
for example the agreement failure in him runs is reflected in the inconsistency of the constraints case acc and case nora
we have attempted to show that the lcg account of agreement correctly treats a number of cases of coordination which are problematic for the standard feature based account
heading an adverbial modified vp agrees in number with its subject the same number features will have to appear in both the antecedent and consequent of the adverb
it is perhaps surprising that the simple feature system proposed here can handle such complex linguistic phenomena but additional mechanisms might be required to treat other linguistic constructions
for example the following proof of the well formedness of the sentence kim slept can be derived using the rules just given and the lexical assignments described above
in general atomic categories in a standard categorim grammar will be replaced in our analyses with formulae drawn from NUM
complex feature structure analyses of agreement require that certain combinations of feature constraints are inconsistent in order to correctly reflect agreement failure
arabic verbal forms appear in NUM in the passive rare forms are not included
this section sets a framework under which templatic morphology can be described using augmented two level theory
this maims it possible for the affix to have a different vowel according to the inood of the following stem e.g.
since the lexicon declares NUM lexical tapes each lexical expression in the two level grammar must be at most a NUM tuple
the pause monitor watches the length of pauses and signals the utterance planner and controller when the pause length exceeds a given length
the following schema r6 defines the communic ttive action of proi osing a domain plan by using sequence
rule NUM maps an atlix symbol from the fourth tap to the surface
NUM ules r4 and r5 delete the boundary symbols fl om stems and af fixes respectively
although there has h eell significant i esea rch
as a n ordered set of events which determine tile interaction
exl ression is exandued as to whether it
l he tylm it node keyl oa rd
a re often solved by context a na lysis
design lit the next section we describe this idea
NUM b o q fllreru l o qsj nom q59 acc touch to h kandenshi mas u
although non volitional verbs only exl ress non volitional actions volitional verbs are cb ssitied into two kind of verbs
then it is contirmed that there are no excel tion to them at least in the collected sentences
since the ro form has a neutral meaning it does not impose any restriction on the subject
llowew r we do not have enough knowledge about the fob lowing l oints
the eonditional to a matrix clause of the sentence with to expresses a consequence of a causal relation
before considering the constraints of japanese conditionals we had better mention the more basic expressions in manuals
note that the exception is tile case that the subordinate clause is stative or a non volitional action
hence the system must offer the user an alternative and the linguistic form of this utterance might differ with the closeness of the alternative to the original demand
moreover it will not miss material that is classified under an unexpected domain or medium but is otherwise suitable
finally cp iw c ew is calculated as the al proximatc wtlue of i c jw rl c ew i i cp jw c ew i NUM min lcb f gj rcb
ew a pair of a japanese word and an english word the mutually best matched pair when r jw ew r jw ew for any ew d ew and ro w ew r jw ew for any jw d jw
if we included both w and w in the co occurrence set of w and vice versa the differences between the co occurrence set of w and those of w and w woukl decrease
japanese nominal comlxmnds np n n english nonfinal compounds np n n i a n the nominal compounds are extracted from the morphological analysis results by pattern matching
therefore instead of calculating the recall according to its de nition we make a rough estimation using the ratio of the number of correct pairs of words extracted to the number of words in either the japanese or english text
the extracted pairs of words were divided into two groups those which are already contained in the bilingual dictionary and those which are not yet contained in the bilingual dictionaryj the former are insignificant from the practical point of view
tables l b and c show the pseudo recall and the precision in cases a and b respectively lu case a the pseudo recall and precision before feedback were NUM NUM table NUM examples of extracted word correspondences
m j NUM n rcb is the intersection of c jw and c ew whose elements u e pairs of a japanese word and an english word with their frequency
in this section we elaborate on the merits of our method
the method we propose here consists of the same three st eps
in practice it makes sense to combine both types of thesauri
table NUM pp attachment disaml iguation results
NUM NUM experiment NUM mdl v s mle
as each sentence is translated we update the discourse model and keep track of the forward looking centers list cflist of the last processed sentence
an edge s start and end values are vertices that are the respective integers representing the starting and ending time points of the part of the action represented by the edge
the decomposition relationship specified by recipe NUM for example can be view as describeostep s h action plan surface request s h action
as shown in figure NUM this model is composed of five modules a problem solver an utterance planner an utterance controller a text to speech converter and a pause monitor
when a pause exceeds the time limit the utterance planner sends the utterance controller an ut input a domain problem parallel modules domain plan is refined during the planning and articulaton of utterances
the results of an analysis of discourse structure in a dialogue corpus are presented and the fine structure of discourse that contributes to the incremental strategy of utterance production is described
NUM describes the action of getting on a bus and NUM de null scribes the existentional status of the bus ms the precondition of the action
patterns with the least weight are to be chosen as the most preferred patterns
the modifications of that algorithm that we have developed make it available to a larger set of text processing frameworks as we assume a considerably poorer analysis substrate
NUM this rate of accuracy is clearly comparable to that of the lappin leass algorithm which lappin and leass NUM report as 85deg
the persistence of gender mismatches in the output simply reflects the lack of a consistent gender slot in the i ngsoft tagger output
ensuring proper interpretatkm of anaphors both within and outside of quoted text requires in effect a method of evaluating quoted speech separately from its surrotmdingcnntext
within these texts we counted NUM NUM third person anaphoric pronouns of these 231l were correctly resolved to the discourse referent identified as the antecedent by the first author
of course this example also indicates the need fl r additional heuristics designed to connect company with apple since these discourse referents clearly make reference to the same object
quoted text passages are not a natural part of computer manuals and are on the other hand an extremely common occurrence in the types of text we are dealing with
for each candidate the annotation in square brackets indicates its offset value and the number to the right indicates its salience weight at the point of interpretatkm of the pronoun
for the first pruning phase we use NUM NUM and for the second NUM NUM although performance is not very sensitive to this
a second direction which suggests itself is to pursue our scaled down approach to treebank conversion but with more tr u ng data than we have used so far
these permit us to ask about the least enclosing node and about children and parents of this source treebank parse node or of its children or parents to any level of structure
ideally our treebank conversion models should take full advantage of data in the full target treebank i.e. the full atr lancaster treeb k as well as the parallel corpus
more generally the ability to ask questions about the entire sentence and in the future document means that the context is of variable length
thus the probability of each decision depends on features extracted from the context including information about any word s in the sentence and any tags and parse structure already predicted
we can even define and query notions like headword with respect to the source treebank parse although this would involve appreciable work
strictly speaking we estimate relative likelihoods rather than probabilities since we make no attempt to normmize over all possible parses for a given sentence
for instance if the speaker chooses to say the man for the subject np then the whole sentence can null not be he was angry with the man
this is good for easily modelable or reduced constraint set problems but in the case of pos tagging or wsd constraints are too many and too complicated l o be written by hand
this is because the constraints used in this case are few about NUM and only cover a few specific error cases mainly tile distinction past participle following verbs to have or to be
next steps should be adding more constraints either hand written or automatically derived on word senses to improve performance and tagging each word with its sense in wordnet instead of its file code
we can coinbine this task with pos tagging since t here are also constraints between the pos tag of a word attd its sense or the sense of a neighbor word
r for g e tdeg v the set of constraints on tag i ieor word j in which the involved variables are exactly those of g
the model used is lie tblh wing each word ill the text is a variable and may take several hfl els which are its pos tags
the first case applies to lexical entries in which c is specified as tl
thus short definitions or definitions by synonym are penalized
if we now consider the finite set of paths occurring in definitional statements associated with some node that set will not include all possible paths of which there are infinitely many
in this paper i only consider classes of noun s but the process described here can also be applied to other parts of speech
on the other hand the type or denotes simply a set of pairs of objects that do not occur together in discourse structure
the verb headnoun patterns approach that of a true verb obj analysis by including a normalization of passive constructions as follows noun have
this kind of information however is corpus specific and therefore needs to be adapted specifically to and on the basis of that particular corpus of texts
a case could be made for including also every instance of the class c m because in principal every animal could be eaten
we can arrive at classes of systematically polysemous lexical items by investigating which items share the same senses and are thus polysemous in the same way
out of the NUM NUM noun stems that were derived from wordnet NUM are to be viewed as true homonyms because they have two or more unrelated senses less than NUM
such conflicts could of course just be ruled out by appealing to their inconsistency which following a logical tradition is grounds for ruling the description to be improper
and the past participle inherits from the root come mor past participle is equated to come mor root i.e. come
of course these two particular forms have more in common than simply being verbs they are both instances of the same verb love
in keeping with its intendedly minimalist character it lacks many of the constructs embodied either in general purpose knowledge representation languages or in contemporary grammar formalisms
computational linguistics volume NUM number NUM is exactly what we want it represents the actual information we generally wish to access from the description
formally speaking the local inheritance network controls the distribution not only of simple values but also of global descriptors as we mentioned above
recall that local inheritance establishes a network of weak equality relationships among node path pairs and these equalities are used to distribute values across this network
finally as we shall discuss more fully in the next subsection value here technically covers both simple values and global inheritance descriptors
this operation is local in the sense that each step is carried out without reference to any context wider than the immediate definitional statement at hand
this node defines the standard truth tables for all the familiar operators and connectives of the propositional calculus expressed in polish order rather than infix order
the first local inheritance is always specified explicitly while the second global inheritance is specified implicitly in terms of the first
euablement is most regularly expressed by the imperative infinitive
this is different from the generation rose since enablement requires the further intervention of an agent and it need not be the same agent to bring about the fl eventuality
the range of rhetorical relations available for the expression of generation is however the greatest of the three languages consisting of a superset of the relations adopted in french and portuguese
NUM NUM for and before are ambiguous
presented first rather than the generating action
figure NUM expre ions of enablement french
the denominator of NUM is constant so maximizing p d s b over d for fixed s b is equivalent to maximizing the product of the numerators af dis b
the score for a parse in equation NUM then has an additional term NUM l p ti is the product of probabilities of the tags which it contains
two of its children x and y are separated by a comma then the last word in y must be directly followed by a comma or must be the last word in the sentence
there are four estimates el e2 ea and e4 based respectively on NUM both words and both tags NUM j and the two pos tags NUM hj and the two
second for each word wi the tagger can provide the distribution of tag probabilities p tiis given the previous two words are tagged as in the best overall sequence of tags rather than just the first best tag
the triple of non terminals at the start middle and end of the arrow specify the nature of the dependency relationship lip s vp represents a subject verb dependency pp lip lip denotes prepositional phrase modification of an lip and so on NUM
auxiliary verbs adverbials or negation occurs optionally in these statements and the infinitival clause need not follow immediately
a prayer the bishop s blessing and the nuns new and old filed out to the cloister
the old man turns to the young one and says the time has come for a few questions
once again it is the syntactic construction and not the modified noun that is the relevant indicator
the sense of side is therefore a more reliable indicator of the sense of right than is the noun itself
body parts are less well represented in the co occurrence sentences for hard those that occur john s justeson and slava m
for example predicate adjective usage indicates the correctness sense of right which is clearly manifested throughout the aphb corpus
freezes in which the adjective itself has no independent sense e.g. hard cash and short cut
in addition we exclude the minority of instances that have definable senses that do not fall within these two groups
compound sentences which consist of multiple full clauses also have multiple cb c data
therefore the centering algorithms currently under discussion are not able to handle naturally occurring discourse
in heiner mflller geschichten aus der produktion NUM berlin rotbuch verlag pp NUM NUM
we have shown that such an approach is not appropriate for some types of complex sentences
the most interesting cases are the ones for which the performance of the different strategies varies
hence the most preferred antecedent of an intra sentential anaphor is a phrase which is also anaphoric
the reason for this is that the method does not consider the inter relationships between the extracted strings
thus it is difficult in general to hand compile a dictionary that contains these kinds of collocations
join to form a new constituent
li t lp labeled recall precision
rmtge of collocations because they are specialized to n l s o1 sillglc words
more flexible interrupted collocations are acquired level by level by iteratively combining the chunks
in table NUM involw domain specilic jargon which can not be const rueted
table NUM percentage of dependencies vs distance be
iteratively repeat this procedure and construct a tree level by level
the head child of each constituent is shown in bold
this makes the parser less sensitive to tagging errors
for german hinrichs and nakazawa NUM the celrg moves an element from the comps list of a verb to its si ash set
null once again undesirable consequences of overapplying an lit under unification can be avoided if applicability of lrs instead requires subsumption llypothesis b
the value of the feature slash contains those items that are realized in left dislocated position e.g. as a topicalized constituent in sentenceqnitim position
consider sentences NUM and NUM and the corresponding centering data in table NUM cb backward looking center the first dement of the pairs denotes the discourse entity the second element the surface
such a grammar can be used either for generation or input
if the system fails to give needed information the user will cease to function effectively
an example of this type of reference from the actual system is shown in figure NUM
our system uses complexity numbers as shown in figure NUM and seeks a minimum complexity utterance
this type of component evaluation within the context of a system is currently much harder to accomplish
the actual values of the constants are obtained by training continuously as the user operates the system
eventually this drives the system to try ordinals to which the user responds more quickly
for most students this was only the second or third time they had debugged a program
in the system we ve just described however the process is much easier
NUM a xyz president and ceo john smith
if the event is an in event the post holder database is searched
single recognizes succession events in which one person is explicitly mentioned
double recognizes succession events in which two persons are explicitly mentioned
each entry consists of a head word and a list of usages
e she9 wins a lot to of trophies
table NUM sample definitions of abbreviation s
the dependency tree in figure 2b is output as NUM
for the walkthrough article all three fillings happen to be incorrect
we showed that broad coverage and efficiency can be achieved by principle based parsing
as can be seen in table NUM about half of word form tokens in swahili are at least two ways ambiguous
the share of five ways ambiguous tokens is NUM NUM but the number of still more ambiguous tokens decreases drastically
the verb root is in the middle and verbal extensions used mainly for derivation are suffixed to the root
they are generally in positions where the governing noun is beyond the current clause or sentence boundary on the left
the word nchi is disambiguated with a rule relying on the ncl of the following genitive connector gen con
it is also possible to refer to more than one context in the same position
a target may be also a set which is defined in the sets section
ambiguity in tokens amb w ambiguity in unique word forms
typical of such first level rules are those where disambiguation is done within a phrase structure
in a derivational framework the grammar converts underlying forms to surface outputs via transformations the input and output share the same space figure la
in this section we will explain how japanese english collocations are constructed from word chnnks extracted in the previous stage
in the example above it is used to point to the situation that a has been talking about as something already established or agreed upon to be difficult and thus can be interpreted as a solidarity politeness operator which reinforces the common ground between the interlocutors
there are also content downtoners such as little a bit just and so on and the use of colloquial expressions such as to give a hand instead of to help which trivialize the action mentioned in the utterance
a 3y sister y x a married x y this reading which is expre ssed in english by NUM NUM some physicians have a sister to whom they are married
NUM NUM sister z n married z according to these conditions the back expressions NUM and NUM were adequate representations of NUM and NUM
but if we consider discourse representations in more expressive languages e.g. the language of an intensional logic it becomes cleat that we have to make only those consistent pieces accessible which result froln tlrstorder consequences of the discourse representation
therefore we have to demand in addition that each resolution deduction starts with a pair of clauses a e mp and b from the discourse representation where b contains an occurrence of the predicate representing one reading of the ambiguous lexical item
iq om a logicm point of view the resolution of a lexical amtfiguity is usually reconstructed by an inference process which rules out a reading if our concet tual knowledge contradicts this readiug in the given ontext
NUM resolving lexical ambiguities is a problematic task since it involves different sources of linguistic and nonlinguistic information intbrmation about the context of a sentence in a discourse about the meanings of the words and about the world
although these approaches can handle the problem of disambiguating information arbitrarily far away the whole context is available as a premise without any fllrther restrictions they run into tractability problems which exclude a practical application
but these counterexamples do not provide conclusive arguments since the expressive power needed in order to formulate these eounterexamples is still rather weak and one could counter by moving a little bit of expressive power around
null although it nay turn out that the disambiguation problem is in fact undecidable if world knowl null edge is also used for disambiguating inferences we assmrm that resolut ion restricted to conceptual knowle dge
in order to see this consider the settheoretic versions of the satisfiability conditions of NUM and NUM for a model with interpretation flmction z given in NUM and NUM
we propose a definition of structm al coml h xity such that sentences ranked by our definition as more coml h x are gen rally more dii ficult lbr humans to process
the definition of structural complexity is based on the assumption that the shorter dependency links are easier to es tablish than longer ones where the length of a dependency link is one plns the nmnber of words between the head and the moditier
it was 31so el served in bach et el NUM NUM that for someone with weu NUM limited competence in english 3nd either of the other langu3ges the p3tterns in l utch and germ3n seem to be more difficult to process 3nd pro i road coverage parse rs
i ll notiou of structural complexity proposed in this l apc r oilers explanations or a set of seemiugly unrelated phenomena we will show dlat the definition of structural comph xity explains why a i utch sentence involving cross serial dependencies is sliglrdy easier to underst md than a corresponding cenl er embedded german sentence
in 7a the extraposition of pp with a mustache increases the length of the dependency link between man and with by NUM but reduces the length of the dependency between talked and yesterday by NUM therefore the structural complexity is reduced by NUM as a result of the extraposit on
when tested on a large separately collected data set our program performs better than the default strategy of picking the most frequent sense
this strategy yields better results as indicated by a better performance of lexas compared with the most frequent heuristic on this set of words
they all share the same interlingua ilt which is a special case of lfg or feature structures
feaspar was trained tested and evaluated with the spontaneous scheduling task and compared with a handmodeled lr parser
when building a speech parsing component for small domains an important goal is to get good performance
the networks spilt the incoming sentence into chunks which are labeled with feature values and chunk relations
however all work with esst and ilt empirically showed that there is no need for structure sharing
relations are shown in square brackets and express how a chunk relates to its parent chunk
this observation suggests that for semantic analysls structure sharing is statistically insignificant even if its existence is theoretically present
this paper is organized as follows first a short tutorial on feature structures and how to build them
three the approach has not been evaluated with real world data but with highly regular sentences
this connection principle is based on the microfeatures in the lexicon that are relevant to a particular network
the analyser correctly handles typical expressions found in our texts including examples NUM NUM see table NUM
these four phrases involve the object or theme of action angioplasty i.e. what the angioplasty operates upon
a pp headed by a functor can not be an adjunct rood none
in terms of lingware development this means that lexical ambiguities have to be avoided for analysis
recursive patterns are described in the programruing language awk
formal constructs known to be computationally expensive are not available NUM
alep is a promising platform for development of large scale application oriented grammars
the 2nd pers sing morpheme st e.g.
NUM defines cardinal numbers in letters
the head dtr chosen by in is an npacc
the tag usr is used if the text is tagged by a user defined tagger
encoding the missing stems which were very few ensured complete tagging of the corpus
this dag acts as filter towards other dags
a substantial speedup can be gained that way
for this purpose a set of heuristics that combine morphological inflection derivation and compounding as well as non morphological lists of endstrings coupled to their syntactic category knowledge
a new large test set of NUM tokens of NUM neurosurgical reports was fed to the t l to see how robust it is when confronted with the vocabulary of a comt letely new domain
in this paper we want to describe a tagger lemmatiser for dutch medical vocabulary which consists of a full form dictionary and a morphological recogniser for unknown vocabulary coupled to an expert system like disambiguation module
section NUM NUM test engine must reduce as much as possible tile potentially valid analyses to the one s effectively applicable in the context of the given input sentence NUM
all these taggers use a rather restricted tagset
those entries are asserted as temporary canonical form lexicon entries and do not need to be calculated again by the recogniser part of the t l when encountered a second time in the submitted text
while nmnerical data can easily be stored and processed for archiving and research purposes free text is rather difficult to be processed by a computer although it contains the most relevant information
the word semina is both sg and pl and the following pronoun zote which has the pl reading solves the problem
the phenomenon is particularly evident in verb structures where different sets of noun class markers add to the ambiguity of the same verb form
by first disambiguating noun phrases and genitive constructions the use of otherwise too permissive rules becomes possible when clear cases are already disambiguated
if the rule system tries to remove all readings of a cohort the target listed in the section preferred target is the one which survives
while the reported ambiguity counted from word form tokens is generally much higher than that counted from word form types in swahili the difference is small
the genotype depends on the tagset but not on any particular tagging method
out of all the possibifities outfined above none seems feasible and robust enough
NUM determine concise tagset based on trade off between tagset size and computational complexity
note that we are only considering unigrams of genotypes which tend to overgeneralize
the fst shown in figure NUM is part of a much larger fst containing NUM NUM million ares
we explored the morpho syntactic ambiguities of a language basing our experiments on french
table NUM clearly demonstrates that the contextual information around the genotype will bring this percentage up significantly
table NUM coverage in the training corpus of n gram genotypes that appear in the test corpus
in order to illustrate this table NUM and table NUM show convincing results using this approach
once morphological analysis is completed ambiguity of words is computed in order to locate the difficulties
restrict the arguments they may lie applied t o
i exemplifies the layout of a fu f grammar
table NUM lexical rules for adjectives
that is the qlf and udrt semantics coincide with respect to truth conditions of representations in corresponding sets of disambiguations
the method has to be extended to a more extensive fragment to prove or disprove its mettle
l xnote that the conclusion udrs k i l can be collapsed into the fully specified drs
a correctness criterion for the translation can be defined in terms of preservation of truth with respect to an independent semantics
proper names proper names 7r always end up in the top level drs it
the u drt construction principles distinguish between genuinely quantificational nps indefinite nps and proper names
can ensure that no initial assumption contributes more than once to any deduction by requiring that wherever two tbrmulae are combined their index sets must be disjoint
hence we can see that we have already gained the key benefit of a chart approach for psg parsing nanmly avoiding the need to recompute partial results
not that tim assignment of term variables in the apt roach in general is such that other eases of accidental binding i.e.
however this move accords with general categorial practice where it is standm d to require that each deduction rests m at least one assumption
note that a single argument position inay give rise to inore dmn one addil ional assumption and so in fact all index set that should be recorded
linear logic is an example of a resource sensitive logic requiring that in any deduction every assumption resource is used precisely once
crtain indices are a subsel of those of l he argumellt but not that tlmy are a proper subset l hereof
minor premise of an elimination inference consists of a sequence of NUM eliminations followed by a sequence of NUM introductions
more particularly the reasoning needed to derive c is liable to involve hypothetical elements whose involvement is driven by the presence of some higher order type elsewhere
NUM NUM note that expressions that contain the crossproduct x or the composition o
figure NUM transducer encoding NUM rnd NUM every arc
if contexts are specified in opposition to the above example then they are taken into account
the last transducer in NUM replaces the suffix cut by the symbol suff
a l lcb on the lower side gets mapped to li or u lcb on the upper side
all intermediate transducers mentioned in this section will contribute to this finm t ransducer bnt will themselves disappear
the replacement of one substring by another one inside a context requires the introduction of auxiliary symbols e.g.
because of this overlap we could not replace both substrings in parallel i.e. at the same time
such a compact model requires relatively little computational effort to induce and to apply
the structured meanings proposal distinguisires between proper and quasi soes
a possible solution for g NUM is with which focus
we take the identification of prosodically prominent elements as given
ithongh in fact our definition is more syntactic than rooth
second how does it extend to a dynamic semantics e.g.
quasi and proper soes which naturally belong together
as we have seen this type of approach is a plausible way to avoid undergeneration
however this theory faces a number of methodological and empirical difficulties
itowever we make quite restricted use of such interpretation
additionally to the above mentioned components there also exists a generic graphical editor for text items and an html interface to the netscape browser which performs marking of the relevant text parts by providing typed parentheses which also serve as links to the internal representation of the extracted information
ve now present intermediate results on training and testing a prototype implementation of the system with sentences from the wall street journal a prominent corpus of real text as collected on the acl cd
other parse actions include add into which adds frames arbitrarily deep into an existing frame tree mark which can mark any slot of any frame with any value and operations to introduce empty categories i.e.
verb adj np whether or not the adverbial alternative of frame1 the top element of the input list is an adjectival degree adverb the specific finite tense of frame i e.g.
the most frequent parse actions are shift which shifts a frame from the input list onto the parse stack or backwards and reduce which combines one or several frames on the parse stack into one new frame
a sentence has a correct operating sequence opseq if the system fully predicts the logged parse action sequence and a correct structure and labeling str l if the structure and syntactic labeling of the final system parse of a sentence is NUM correct regardless of the operations leading to it
as shown in figure NUM the action r NUM to vp as pred 0bj pat for example reduces the two top frames of the stack into a new frame that is marked as a verb phrase and contains the next to the top frame as its predicate or head and the top frame of the stack as its object and patient
null our system performed better than the commercial systems but this has to be interpreted with caution since our system was trained and tested on sentences from the same lexically limited corpus but of course without overlap whereas the other systems were developed on and for texts from a larger variety of domains making lexical choices more difficult in particular
for labeled precision are negative because a higher better labeled precision correlates with a numerically lower better translation score on the NUM NUM best to NUM NUM worst translation evaluation scale
this ordered left to right parsing is much closer to how humans parse a sentence than for example chart oriented parsers it allows a very transparent control structure and makes the parsing process relatively intuitive for humans
given a particular parse state and a feature the system can interpret the feature and compute its 2s synt elem designates the top syntactic level since NUM is negative the feature refers to the 1st frame of the parse stack
transitivity and foregrounding in news articles experiments in information retrieval and automatic summarising
the notion of transitivity provides a measure against which clauses can be scored
the corpus is being used in two text processing experiments
i played the piano i am playing the piano
in contrast the contextual information relating to characters and environment is backgrounded
suitable empirical evidence fbr settling this issue comes from the counterparts of the portuguese examples in NUM where the rellexive is replaced by tlhe pronoun ele ruled by principle b NUM presents examples where the pronoun and its antecedent occur in the same arg s list and they are equally oblique
now when it is principle b that must be validated it must be checked whether a given element x does not locally o cemmand another element y if x and y are not in the same arg s list they do not locally o command each other irrespective of the option tbr a linear or a non linear obliqueness
there are languages in which the reflexives though they must be locally bound can be bound only by a subject
in this section i argue this can be done by letting the arg s value have a non linear ordering
it is easy to check that the correct predictions are made if the relevant o command relations are established on the arg s list the reflexive is now coindexed with a more oblique element in NUM a NUM a and with a less oblique antecedent in NUM a NUM a
john was shaved by himseh isubcat np o ni ana rcb in cennection with this possibility for lexical rules to change obliqueness relations it would be interesting to lind cases where lexical rules change o command relations in a way that the result requires a branching configuration
unknown words were considered to he those which could not be divided into morphemes appearing in the learning corpus of the markov model
problem can be solved e sily by the optimal gradient method because both the objective function and the feasible region are convex
in general a probability distribution can be regarded a s a vector so the concatenatiori of two vectors is also a vector
this is one of the advantages of our method over heuristics based on character type which can never recognize mixed character words
candidate for unknown words were limited to strings of two or more characters appearing in the corpus at least ten times and not containing any symbols such as parentheses
as an example let us take the string NUM which is used in tile corpus only as a verb and an adjective
the environment of each pos is obtained by calculating statistics on all contexts that precede and follow the pos in a tagged corpus as follows NUM
the sentence boundary is not included in the pp calculation
the first term NUM can be reduced to an n gram lm
hierarchy if there exists i t i k such that for very j l j i
let a tuph of formulas NUM be broken into disjoillt parts q51 NUM NUM NUM a
h x figure NUM result of adjoin left
let ci x and p x be formulas with th same nulnller of fre w riables x
the correct partial parse of the word history when predicting barked is shown in figure NUM
as illustrated in section NUM t his assumption is not necessarily valid in practice
in this paper we propose a method of learning dependencies between case frame slots
NUM NUM depend moles ound NUM NUM
a classical method is chow liu s algormnn for estimating a nmltidimensional joint
btmm d for some randomly s h ctcd verbs
experinlent can be inlproved by using the acquired knowledge of dependencies between case slots
distribution without loss of generality any n dinlensiorlal joint distribution can be writl en
in this paper wc view the problem of learning as
moreover the type hierarchy mlows concepts hence words to inherit reference models from more m stract olmepts thus enabling more sitaring mm modularity
ilowever inadequate expai sion8 are solnetilnes made due to lack of lnodels or to their complex ity which makes the heuristic principles not selective enough
NUM is then handled as a metonymy where the stenosis and the stenosed object enter a state thing alternation stenosis is turned into an object
semantic analysis then consists in solving recursively every grammatical link starting from the sentence head predicate and then joining the obtained conceptual chains to build the conceptual representation of the whole sentence
overall recall and precision were measured at NUM NUM and NUM on the o ling task and NUM and NUM on the questionnaire task
rather than including the knowledge needed for this task in the semantic lexicon or in a specific rule base the program will examine the domain knowledge to resolve the link
l his is a useful convention but we also need to be able to refer explicitly to the beginning or the end of a string
we split the set of parallel replacements into two groups one containing only replacements with empty upper and the other one only with non empty upper
the difference between them is where the left and the right contexts are expected on the upper or on the lower side of the relation i.e.
if an upper contains the empty string but is not identical with it the replacement will be added to both groups but with a different upper
if we used the same bracket for both this would mean an overlap of the substrings to replace in an example like x l la l
we define such a relation by changing af the part not containing any bracketed upper in expression NUM into
table NUM best results stopping before conw rgence
then several options for creating subgrammars from the complete grammar will be discussed
consider a subgranunar which contains elnpty productions or unary coercion rules
finally start and end specify the start end point of a spanning edge
bottmn up hypotheses are emitted by the syn parser and sent to the sem parser
figure NUM xt erimental results of syn sem separation
the parser extends the chart on its own through prediction and completion steps
this is achieved by sending NUM ack fais i icd hypotheses
partial evaluation means here to substitute type symbols by their expanded definitions
firstly termination inight change ill case of tile sul grammars
however in practice l his i roblem might occur
secondly all of undetermined parts of the semantic representation are filled or settled by some kind of inferences based on ttie donlain knowledge
as for the conjunctive reba the fact that tile conjunctive represents some causality means that the matrix clause does not have a request form
one of them is tile cl ss of verbs in volitional use the other is the class of other non volitional predicates
first of all as we expected before the distribution of the use of reba is different from those of tara and nara
to solve these problems we have to have a computer assisted system tbr processing japanese manual sentences especially tbr understanding manual sentences
the difference of constraints of these expressions are shown in the following sentences which are the variants of the sentence NUM
controls allow defaulting which is illegal for the network l efaulting consists of assulning some fact when no information of that fact s type is e xpressed explicitly
t is NUM heroforo ilnl org utl lo cnsuro r01cwmt informal ion is rol rcsc h NUM l loca lly
similarly the agent will be referred to by the natne i oi ita as this is the only agent so far which uses serene
such unidire tional events are beneficial to the leterntinism of search since they restrict the number of arcs that can be traversed from any node
the paper introduces two i rol crties distributedness and nonlinearity of networks which directly relate to the efficiency by which knowledge is obtained
as such the internal representatiou must be richly expressive mttural with respect to natural language and e icient
lib example NUM oixi a may believe that l lcb oberto believes that every l rmer owns a donkey
since the representation ret resents different concepts i y different nodes there inust be a means to state that two coi cepts red to the same objeet
difficult among t he corn categories
this do this pointing at a picture on the screen and clicking the mouse during the first his and then choosing all itmn front a lllelltl during the second
the rules define how to in l erl rei a n hupera i ive sentence like l elete this circle wil h va riet ies of expressions
this pa per described the nmlti nloda l method
metaphor some sort of centraj application object is in cha rge and must send messages to the screeu the mouse and the voice system asldng for input upon activation
the syslenl must realize that the first point is say a pict ur NUM of a particular animal a ud the second is the tttetm item fly
is attached by an elaboration hnk th6 new hypothesis of a topic structure s given m figure NUM at that moment two new proposltaons have been attached tothe theme so that the theme has three extensions
thc iinusually low i aseliue t crj orillance i esults l olll kl conil iuation of t shiajl l ilot lr illing set and t inil lly xten e i t g set
model a finally model a is scored the same as model b except for the second factor in NUM sthe third factor depends on e.g. kid i c NUM which we recover fl om the span signature
owing to the particular re ursiw strategy the p trscr uses to bre tk up the s tl n e the statistic would be measured ttld utilized only under the condition lescribed above
since our sentences have links as well as tags and words suppose that afl er the words are inserte l each senl ence passes through a third step that looks at each pair of words and ran lotnly decides whether to link them
it is uscflfl to look into thes0 basic questions before trying to tine tmm the performance of systems whose behavior is harder to understand NUM the main contribution of the work is to i ropose three distin t lexiealist hyl otheses abou
each lexical entry is automatically extended with a definite clause encoding of the lexical rule applications which the entry can undergo
all remaining operations of the algorithm will now be charged to some active pair
the second author is also a member of the center for language and speech processing
let nl n2 ntl t NUM be the postordered sequence of all nodes of c we write
in what follows trees that are equivalent are not treated as the same object
we review in the following subsections some terminology that is used throughout this paper
proc update oldset newset j for each node n e oldset
state m17 qz0 lcb n25 rcb and state m16 qs
in this way each active node is charged an extra amount of time o p
the periodic node p of q under consideration is indicated by underlying its label
note that among the NUM NUM possible states only NUM are useful
the choice of formal and informal predicate forms in japanese and the choice of distant and familiar second person pronouns in french and german are examples of lexically encoded discernment markers
the process starts with the input string and the current dictionary entry leftaligned
for instance mone receives the strange pronunciation moni by analogy with anemone
however all these observations can arguably be explained by a single route
this is at variance with the notion of two independent routes to pronunciation
another input which thils to produce a pronunciation is aardvark
it still selects primarily on the basis of shortest path length
figure NUM l cdina and nusbaum s pronounce
the process is repeated for all words in the dictionary
table NUM results for pba of pseudowords with both dictionaries
figure NUM partial pronunciation lattice for the pseudoword shead
spoken language expressions however tend to deviate from conventional grammars and a system consisting of layers of rule based modules is often too brittle to handle naturally occurring spoken input
the expected usage count for a rule can be computed as
a state produced by prediction is called a predicted state
a state produced by completion is called a completed state
therefore in the best case the following literal translation of sentence NUM might be obtained NUM to reject something like that is difficult
they concern the speaker s intention of how to say an utterance as opposed to what to say propositional content of the utterance
the speech recognition program converts the speech signal to a string of word hypotheses possibly introducing additional errors and distortions which results in the recognizer output
in our case the attributes are the different linguistic constructions a noun occurs in headnoun verb adjective headnoun modifiernoun headnoun etc
recall on the nouns in corelex is between NUM and NUM while precision is between NUM and NUM
this edge is deemed far more likely to serve as the basis for a correct full parse than any of the edges spanning substrings of this phrase those edges too are therefore pruned
firstly fewer utterances time out due to slow processing secondly the reduced space of possible analyses means that the problem of selecting between different possible analyses of a given utterance becomes easier
many people do this but one can not help feeling that something is being missed intuitively there are many domain independent grammatical constraints which one would prefer only to need to code once
however if a substantial training corpus is available to provide reasonable estimates of the relevant parameters the immediate context surrounding s will usually make most of the locally possible analyses of s extremely implausible
although people differ widely in their judgements of whether a given translation can be regarded as acceptable it is in most cases surprisingly easy to say which of two possible translations is preferable
the coverage of the specialized grammar is a strict subset of that of the original grammar thus any analysis produced by the specialized grammar is guaranteed to be valid in the original one as well
similarly edges for one as a determiner and as a noun are pruned because when flanked by two other numbers one is far more likely to function as a number
the unigram score the probability of correct null ness of an edge considering only the tree of grammar rules with words or word classes at the leaves that gave rise to it
these bindings are subsequently used to generate single word and or multiple word terms for indexing
other actions such as sketchiug wiili a light i ell sca iltlilig NUM ocuillent or sl ea king
this is simply a list of all the discourse enities realized in that sentence ranked according to the theta role hierarchy found in the semantic representation
those items that do not play a role in is of the sentence as the topic or the focus form the ground of the sentence
null in section NUM i discuss the information structure and specifically th topic and the focus in naturally occurring turkish data
a discourse old topic often serves NUM o liuk the sentence to the previous context l y evoking a familiar and smient discourse entity
this process yields the intended propositional content next depending on the speech situation and discourse context the speaker applies certain pragmatic utterance strategies
it is not sufficient therefore to recognize the surface form of each pragmatic strategy and directly transfer it to the same surface form in another language
the following patterns are also needed
therefore the regular expressions need to be rather generous
product and company names can be very unconventional
these occur both with and without numerals e.g.
sechsundzwmmig is literally six and twenty
so the single input john sees mary after being processed by the th component will take the p and p mark the beginning and the respective ending of the recognized paragraph structure
the other tags must be interpreted analogously
node id is NUM NUM o NUM NUM NUM
in co occurrence based similarity calcula tion
here while pointing at a point
pa ths between a gents a re limited
modes include speech keyboard a d mouse inputs
drawing NUM ool along with tim ttmlt imodal nlethod
rules of a ny combination of these three modes
then the totaj number of the multi nloda l
they a re used and interpreted idemica lly
when people engage in face to face dialogues the focus is usually on establishing and maintaining a good relationship among the interlocutors rather than mere transfer of information
if a parse is found it must be the highest ranked parse by the model as all constituents discarded have lower probabilities than this parse and could calculating scores
area estimated hy the proposed method contained the node theft the word really belonged to
one continuous microfeature value v t t for a word w is set automatically to the percentage of feature value occurrence given that word w
since a feature normally only occurs at a certain ctmnk level the network is tailored to decide on a particular feature at a particular chunk level
the subjects calendars have conflicts so that a few sug null gestions have to go back and tbrth before finding a time slot suitable for both
the rules are shown in figure NUM
we assume that ius a re realized by grammatical lievices a clause realizes an NUM u an inteljectory word realizes an 1u and a tiller term shows the end of an iu figure NUM shows pa rt of the transcription of a dialogue where a diahlgue participant prol oses a dommn l lan
in this domain the domain plan is a sequence of actions a t a5 a6 and at moving from the musashino center to kichijoji station by bus moving to shimokitazawa station by tile inokashira iane moving to aiko ishida station by the odakyu line md then moving to the atsugi center by bus
for example althougil iu NUM in figure NUM descril es only a part of a domain action it is regarded as ail iu siil ce it has a e pula desu an NUM a sentenee linal t article ne
tile problem solver first makes an abstract domain plan which is a sequence of three actions el a2 and a3 moving from the musashino center to the nearest station by bus moving to the station nearest the atsugi center and then moving to the atsugi center by bus
he context model records the information that has been conveyed and tracks the attentional state
when the utterance controller is not given utterance plans within the time limit it produces a filler term
we l resent a eomlmtational model of incremental utterance l roduetion using the line structure of discourse
NUM NUM persuadea plan al a2 a3 NUM NUM surface coinmunicative actions sllrfa e commnnicativ actions used here are sv rfacc desc cvent e c at utter expressions to descrit e domain event e ilq all event having content c and des ribe attitude at toward e
from the musashino center basu de moyori no eki made bus by nearest station to by bus to the nearest station kichijoji made desune ikimasu pn to copula go to kichijoji station go in the above the redundant information is not restated according to pragmatic constraint NUM
since the nonterminal symbols are atomic one merely needs to check for left recursive symbols so that the computation terminates
the splitting of a sentence in ee is done on the corresponding cs
this translation uses the results of the syntactic analysis syntactic tree
they are involved respectively by the said buy and allows verbs
we focus more on the mechanisms that handle these different kinds of rules
NUM the problem of initial anaphors is then resolved
we first present how intrasentential antecedents occur in embedded sentences
however this mechanism is not suitable for intrasentential cases
b they played it every day after school before dinner
factors that influence embedded sentences are mainly semantic features of verbs
null NUM NUM what needs to be improved in the focusing approach
we have th us a h gic d basis for the primitives of most object models and a framework for the
a type underlies extensional objects a change in the properties of a type entails the same change in the associated extensional objects
n c de aomin al ion we also call associate to a name of an object other synonyms
b scores better on precision soft but there is not much difference for precision hard
the resulting figures are in table NUM and table NUM gives the comparative items
the right hand side consists of a global path specification one of whose component attributes is itself a descriptor that must be evaluated before the outer path can be
there is also good evidence in the speech error literature for the chtim that people plan in abstract terms
linguistic reasons as mentionned already tile otr er of words does not necessarily parallel the order of thought
the basic question that arises in this context is the following when do we process what
this being so dictionaries may play a fundamental role in guiding and potentially modifying non linguistic thought
yet not all of them fit equally well the initial message and lor quite different reasons
this is correct but lot reasons of economy size of the figures we ve skipped this step
NUM this being so the question arises whether to run can be considered as a valid candidate
step NUM if we were to characterize the boy in terms of size we might get small
if the remaining sister categories of the left corner can be parsed then the mother category of the rule is the result tbr the corresponding substring and the algorithm continues recursively until the entire string is covered by a category if the category of an expectation was specified it must match the category found
various types of features indicate the type of name parts of speech pos designators morphology syntax semantics and more
thanks also to kevin knight for his early suggestions and to the information sciences institute for use of their facilities and resources
first the english trees generated from the feature set optimized for english are applied to the spanish text e e s
safetek can be recognized as a company name by utilizing the preceding contextual phrase and appositive the new company
the second and third spanish experiments s e s s s s require in addition pre tagged spanish training text using the same tags as for english
the author would like to offer special thanks and gratitude to eduard hovy for all ofhis support direction and encouragement from the onset of this work
the third experiment proceeds like the second except that minor adjustments and additions are made to the feature set with the goal of improving performance s s s
the weighted average of p r for companies persons locations and dates is NUM NUM see table NUM
the metrics used were recall r precision p and an averaging measure p r defined as
these messages have been hand tagged with respect to the relevant information
the recognition part an fst operates on a stream of tokens
the filtering rules are determined on the basis of unannotated corpora
so tile ilypothesis is that h s awareness of the presence of a certain choice point in executing a set of instructions affects the choice of one preventative expression over another
an information extraction core system for real world german text processing
currently it includes NUM NUM lexical root entries subgra mmars
concerning the above mentioned corpus the lexicon covers about NUM
the speaker may also try to invite the addressee s involvement by using hearer oriented question tags such as right all right okay would you or will you or by using devices to attract the addressee s attention such as look listen hey and informal or affectionate address terms
hence we view smes as a core information extraction system
thereby provide an easy means to skip text without deep analysis
first two similarity trees are constructed as shown in figure NUM graph matching is then iteratively attempted by compnting mutual inforlnation fbr groups of word chunks
in general neg tc imperatives are used when s expects h to overlook a certain choice point such choice point may be identified through a possible side effect that the wrong choice will cause
since every word is a potential unknown category word even closed class words if a small corpus is used we ideally need to treat all words of a sentence as possible unknown category words
the second column shows nr the number of np subtrees that had those frequencies in the training set the estimation of no is a special case and will be dealt with shortly
dop2 is a very simple extension of dopi assign all lexical categories to each unknown word and select the most probable parse among the parses of all resulting sentences by means of dop1
as to the first question we are not able to generate the space of unknown subtrees in advance as we do not know the unknown terminals in advance
notice however that the tagger used in this two step approach often uses good turing or a similar smoothing method to adjust the observed frequencies of n grams
as to the computational aspects we can very easily extend the parsing algorithms designed for dop1 to dop3 by allowing the terminals of subtrees to mismatch with the words of the input sentence
we must distinguish between subtrees of different roots since in dop the spaces of subtrees of a certain root constitute different distributions for each of which the substitution probabilities sum up to one
with respect to the parsing of word strings we have shown that the hardness of the problem does not lie so much in unknown words but in previously unseen lexical categories of known words
if we look at the sentences with only known words where dop2 is equivalent to dop1 we see that the parse accuracy of NUM is higher than for sentences with unknown words
NUM in different parsing scenarios the scanning step may well modify probabilities
parsing then proceeds as usual with the probabilistic computations detailed below
however only unit productions NUM can give rise to cyclic completions
this analogy will be explored further in the probabilistic formulation later on
the parse trees for sentences can be reconstructed from the chart contents
scanned scanned scanned scanned scanned odet a 1n circle
the corpus consisted of NUM NUM sentence with an average length of NUM NUM words
obviously the system has to be solved only once for each grammar
many thanks to all my colleagues at rxrc grenoble who helped me in whatever respect particularly to anne schiller marc dymetman and jean pierre chanod for discussing parts of the work and to irene maxwell for correcting various versions of the paper
the s nl type transducer containing all possible subsequences up to a length of three classes is the most accurate table NUM last line s nl fst NUM NUM NUM but mso the largest one
b generation of possible subsequences based on the set of classes we generate all possible initial and extended middle class subsequences ci and c e eq NUM NUM up to a defined length
the aim of the conversion is not to generate fsts that behave in the same way or in as similar a way as possible like iimms but rather fsts that perform tagging in as accurate a way as possible
the main advantage of transforming an hmm is that the resulting fst can be handled by finite 9a maximal length of three classes is not considered here because of the high increase in size and a low increase in accuracy
a similar rate of accuracy at a much lower size can be achieved with the s nl type either with all subsequences up to a no nl n0 type with only lexical probabilities or nl type sec
they use detailed task specific templates and semantic grammars which can recognize various fixed phrases to mark speech act types while skipping over disflucncies in the input
the corresponding liged forest whose start symbol is s s and production set p is
therefore the extraction of a linear so x derivation in l is performed in time linear with the length of that derivation
each sentence in this cfg is a derivation of the given input string by the lig and is extracted in linear time
NUM transition states are ordered where the transition state is determined based on two factors whether cb of the current sentence is the same as of the previous sentence and whether cb is the same as the highest ranked member of c i of the current sentence
walker s version on the other hand uses the following additional rules and constraint constraint for each sentence ui the center cb ui is the highest ranked element of c i NUM NUM that appears in ui
we call object the pair denoted a a where a is a non terminal and a a stack of symbols
lifl le NUM the performance of l tm systems based on kameyama s algorithm method cor lcb e e i rcb corre t a cura cy
since he handles not only zero pronouns but also overt pronouns the exact comparison is difficult but his approach that is based on kameyama s approach yields the performance of NUM NUM if the results for overt pronouns are excluded
of emdunetive postpositions even if the antecedents are searched in the previous tour simple sentences simple partition approach might not yield good performance because tile information of conjunctive postpositions that are between two adjacent simple sentences is not taken into account
since our method that is presented in this paper is based on the centering theory and basically uses only syntactic information we plan to incorporate the semantic constraints that filter anomalous antecedents for zero pronouns and take into account the global structure of discourses
fnjisawa s investigation on NUM NUM sentences of the scientific journal and NUM NUM sentences of the review articles from the newspaper showed that NUM NUM of the antecedents appeared in the previous or current sentence and NUM NUM appeared ill tile previous two sentences or current sentence
when a mouse click occurs nautilus asks the application for the identities of all the objects located at or near the mouse event and then takes the subset of those objects that match the semantics of the verbal phrase which can be determined from predicate context as well for example the word here in have fighter i refuel here necessarily refers to a tanker aircraft
the logical quantifiers and connectives forall exists not and etc have generic procedural definitions as lisp macros so the system developer just needs to develop a so called translation function tf for each of the tinsel predicates in the domain
in two others intervr and interrob the target application runs on another unix machine on the local net so the ifs on the nautilus side must encode and transmit message strings over an ipc socket to a corresponding decoder layer linked into the application
each one has augmented it with a few dozen additional rules for handling application specific constructs like station two sector one eucalyptus latitude forty degrees north longitude ninety five degrees west intervr the town of leipzig interlace and thirty degrees left interrob
to avoid the problems of time correlating speech with graphical input and distinguishing anaphora from deixis we reserve the words this these and here for deictic reference and that those and there for anaphoric reference and allow no more than one plural deictic reference per utterance
this pa per describes a way of overcoming the prol lems using a medium size japanese thesaurus aim large corpus
we view the problem of learning case frame patterns as that of learning a multi dimensional discrete joint distribution where random variables represent case slots
l h similaril y between the word a nd each de in isamap is calculated
in the exl erhnent NUM NUM nodes with the root physical obje t in isamat were used
on the other ha nd the pattern tobu fly shares only two nodes h elicopter aaid aiwlaue
NUM x o o NUM is given a specific l robability value by a word based model
this is because the number of parameters in a class based model is very large compared to the size of the data we had available
for each rela tionship a sea tell is nlade h r nodes that have the sa me relationship
suppose we want to extract the viewpoint o the noun hf i lcb ikop utaa helicopter
we then forlmllale the dependencies between case slots as the probabilislic dependencies between the randonl variabh s in each of these three trtodcls
we also used the NUM verbs and their case frames used in experiment NUM to acquire class based case frame patterns using the proposed method
null there has been no method proposed to date however that learns dependencies between case frame slots in the natural language processing literature
NUM in accordance with the prototypical situation description given in figure NUM the drs for verseheuken is as follows 4clearly these are iust
the asterisk marks the plot for the narcotic sense
the log likelihood factor captures this tendency
the rest of this paper is organized as follows
section NUM describes the approach we have developed
a NUM word weights in our algorithm the weight of a word estimates its expected contribution to the disambiguation task and the extent to which the word is indicative in sentence similarity
the weights do not change with iterations
this yields much more sense presorted training information
telegraphic utterances that omit obvious information can be interpreted as a strategy to emphasize common knowledge among the interlocutors
since ambiguity in associating co occurrence sets does not occur too often and considering the need lbr efficiency we execute either of the two approximate calculations rather than make a precise calculation
while the precision is rather easy to calculate the recall is difficult to calculate because it is a time consuming task to manually identify all the word correspondences in the bilingual cortms
NUM NUM feedback of extracted pairs of words obviously the performance of the proposed method depends upon the coverage of the bilingual dictionary over the corpus
to associate two co occurrence sets whose elements are words in different languages we consult a bilingual dictionary and extract the possible word correspondences between them
in a hilingual corpus a pair of words corresponding to each other generally accompany the same context although expressed in the two diflcrent languages
with the growing wdume of text available in electronic lorm a number of methods have been proposed tor extracting word correspondences from bilingual corpora automatically
we use co occurrence i n a sentence i n which a pair of words occurring within the same sentence is regarded as a co occurrence
the statistical approach utilizes the occurrence frequencies and locations of words in a parallel corpus to calculate the pairwise correlations between the words in the two languages
the recall is the proportion of all word correspondences in a ljwe neglected tile reference numbers peculiar to the patent docmnents because their correpondences are irivial
the probability of a basenp sequence in an unreduced sentence s is then
p bis x az dis b NUM
in this sentence sales and of co occur three times
questions NUM NUM and NUM allow the parser to use this information
section NUM NUM describes how arrows are labeled with non terminal triples from the parse tree
the mapping from trees to dependency structures is central to the dependency model
this section describes the statistical model while section NUM describes the parser
square brackets enclose basenps heads of basenps are marked in bold
there are many possibilities for improvement which is encouraging
cbs is the average number of crossing brackets per sentence
it has also undergone participatory design and user testing with the us marine corps at their training base at NUM palms california with the us army at the royal dragon exercise at fort bragg north carolina and as part of the command center of the future at nrad
null since speech may follow gesture and since even simultaneously produced speech and gesture are processed sequentially the integrator can not execute what appears to be a complete unimodal command on receiving it in case it is immediately followed by input from the other mode suggesting a multimodal interpretation
in the open microphone mode where the user does not have to gesture in order to speak spurious speech recognition errors are more common than with click to speak but are frequently rejected by the system because of the absence of a compatible gesture for integration
if no signal is forthcoming from the other mode within the time window or if interpretations from the other mode do not integrate with any interpretations in the set then the best of the complete unimodal interpretations from the original set is sent to the bridge agent
next the phrasal rules are applied bottom up to find all possible phrasal edges after which unlikely edges are again pruned
as the example above suggests judicious pruning of the chart at appropriate points can greatly restrict the search space and speed up processing
for example the indexed formula i xo yo zo w will compile to give three indexed formulae i xo y lcb j k rcb j z k w we require a inodified elimination rule that will enforce appropriate usage NUM
when the agenda is empty a check is made for any successful overall analsyses identified as described above
serum tic recipe of the combination ai propriate to the original type combination is returned
we here use a natural deduction formulation requiring the following rules oelimination and introduction respectively
this requirement is met by all implieationals as a side effect of the ompilation process
the grammatical link between two words in a sentence expresses a conceptual link between their two associated conceptual counterparts
the semantic analyser relies on a two tier semantic lexicon one for predicates the other for grammatical relations
NUM most specific or highest priority of gr preferences NUM shorter chain length
angioplasty purported obj a rt cry segment
chains that violate gr preferences are discarded and model pairs are explored starting fi om the most specific pair
the method relies on a heuristic path search algorithm that exploits the graphic aspects of the conceptual graphs formalism
the corresponding types angioplasty and segment ii are not compatible and tile fusion inethod fails
let us illustrate the resolution on example NUM an angioplasty of segment ii
after state rel figure NUM some semantic lexicon entries for pred icates and a grammatical relation
such pairs are structured by a partial order based on the generality rank of their members a NUM NUM NUM
far expended little effort on optimizing these phases of processing it is reasonable to expect substantial further gains to be possible
figure NUM a fragment of the joint frequency k u v n u v
k u total number of links in the bitext pr mutual translations i co occurrence pr link i co occurrence pr link co occurrence of mutual translations pr link i co occurrence of not mutual translations pr kin p where k has a binomial distribution with parameters n and p
part of the difficulty stems from the implicit assumption in other models that each word has only one sense
each word is assigned the same unit of probability mass which the model distributes over all candidate translations
NUM find u and v such that the likelihood ratio l u v is highest
when the l u v are re estimated the model s hidden parameters come into play
the linking algorithm creates all the links of a given type independently of each other so the num null ber k u v of links connecting word types u and v has a binomial distribution with parameters n u l and p u
the bitext preprocessor for our word to word model split hyphenated words but macklovitch hannan s preprocessor did not
in some cases hyphenated words were easier to link correctly in other cases they were more difficult
this paper proposes a method for extending an existing thesaurus through classification of new words in terms of that thesaurus
this model was originally developed for document categorization in which a new document is classified into certain predefined categories
in addition clusters are structured in a tree when a hierarchical clustering algorithm is used for the latter approach
in this case one can adopt a top down tree search strategy for similar clusters saving further computational overhead
the wordnet NUM for each pair of noun phrases np1 np2 where np2 precedes np1 in the text a manually constructed decision tree computes a weight using the classification of the heads of np1 and np2 in the wordne t NUM as well as their semantic and syntactic features
if the person holds multiple positions in different organizations or incompatible jobs NUM in the same organization we assume that the person will give up those positions and corresponding records of out events will be created
status unk post president holder john smith org xyz inc status unk if the pronoun he is determined by the coreference module to refer to john smith then NUM will als o cause the two records in NUM to be created
the record NUM is created NUM post chairman holder john smith org abc status in assuming that the static rule had created the records in NUM the in event triggered by named would also cause records in NUM to be inserted into the database
status out the rule double is triggered by the following words NUM succeed accede replace follow succession replacement if the trigger is passive the in person is the subject of the trigger or c commands the trigger
there were NUM errors NUM incorrect NUM missing and NUM spurious many of which were due to failures of the ne module incorrectly treating new york times as an organization caused NUM te errors
which acquired the boston based retailer her8 can not refer to a c commanding element in the same clause it is possible to have immediate contradictions since NUM and slo are made by different rules
two mistakes were made in the following sentence dooner who recently lost NUM pounds over three and a half months the lexical rules classified NUM pounds as money and half as a date
dooner as presiden t the omission of hire in the trigger word list is responsible for a missing in event implied by NUM NUM in addition peter kim was hired from wpp group s j
the constraint that the organization and its locale mus t not be separated by another organization prevents boston to be assigned as the locale of xyz in 9e since retailer is also a kind of organization
the mt ual window size mmy l e limited by boundaries of strllcturml mfit s sm h its sentences or parmgraphs
this is possible thanks to overall redundancy and rep titiveness of information particularly local lcb ontext information in large bodies of text
for products a noun phrase following manufacturer of producer of or retailer of
state o wncd electronics giant thomson s a banldng groul said dw forreal merger of skansl l lcb anken into
our task is now to iucrease tile recall while maintaining or veil increase if possible the precision
the efl ect of bootstrapping is clearly visible in both charts it improves the recall while mainraining or even iinproving the pre ision
slamdinaviskm fmskihla bank m and l hc stmte owned electronics giant homson s a in mddition to the initiml two names
ill addition i ositionml relal ionshil s among l hes words mmy be of importaalce
different methods of computing sws combining sws and parameter adjustmenting for the bootstrapping process need to be explored as we believe there is still room for improvement
a lexical entry in moose has the following components denotation a partial sitspec that defines the c pplicability condition of the lexeme if its denotation subsumes some part of the input sitspec then and only then it is a candidate lexical option for the verbalization
it considers list cf ui of forward looking centers which are the semantic entities realize in ui where ul is the i th utte ance
to generalize the correspondence to an extension rule we need to assume in the domain model a concept like completion state which is to subsume all those states in the domain model that have extreme values an empty bucket a fully loaded truck and so forth
a rule that is parallel to that for the transitive case is given below it derives the tank drained of the water since the of ma eter is optional we can also produce the tank drained which is according to levin preferred
be informative the system has all the information and has the goal of getting across some basic messages about the content of the gallery
but it should be clear that any hypertext system designed to simulate such discourse must make use of a dynamic generation component of some sort
the paper reports on a text handling component two level morphology word structure phrase structure semantics and the interfaces between these components
hu rently there is a trend of lmilding large aisystems in a distrilulted agent oriented manner
if more than one candidate remains take the defaul rcb interpretation that wouhl be used if there were no context iuformatiolt
our context model consists of parse f trees that are obtained NUM y using mi exlstil g gc lwral syntactic parser
the modality of itemized sentences or phrases is of ten ambiguous as a result of the NUM resence of ellipses
snch s nmdifiee modifier relationpronoun referents and the focus of focusing ships and their i ositions in the text
full text processing improving a practical nlp system based on surface information within the context nasukawa t rl vnet ibm icbm
frame clarify consists of a feature e.g.
the data sets are real world data containing spontaneous speech effects
one could expect the maximal nesting depth to cause limitations
the latter also contains spontaneous effects and speech recognition errors
it has two modes learn mode and run mode
feaspar uses neural networks to learn to produce chunk parses
figure NUM chunk parse chunk relations shown in boldface
then each chunk is labeled with feature values and chunk relations
it has one network per chunk level and chunk relation element
the following example illustrates in detail how the three parts work
NUM is ambiguous i ctwc m NUM and NUM inch one of which is not ambiguous
all possible combinations of instantiations form the set of substitutions
in other cases however we observe a diversity of realisations
figure NUM examples of task element expressions
the polarity of processes is always positive
virtually all the causatives occur here
we observe that only positive polarity occurs in ready reference
figure NUM distribution of task structure elements over genres
this framework was chosen for several reasons
sfl stratifies meaning into context and language
we label them respectively procedure and elaboration
elaboration shares features with both of these
the semantic distance between words is calculated according to the relationship of tim positions of words semantic attributes in the thesaurus
computational linguistics volume NUM number NUM we have two statements at wordl that after applying the abbreviation introduced above both inherit from verb word1 syn cat verb syn type verb
the line mor form mor root ing will occur in every present participle form of every verb yet it is a completely generic statement at can be applied to all english present participle verb forms
if the definitional statement for this pair provides a local descriptor then we follow it by changing one or both of node or path and then repeat the process with the resulting node path pair
computational linguistics volume NUM number NUM can be read approximately as if the value for node2 path2 is defined then the value of nodel pathl is defined and equal to it
this distinction between sentences and statements is primarily for notational convenience it would be cumbersome to require repetition of the node name for each statement and statements are the primary unit of specification in datr
the semantics of the datr language binds the two together in a declarative fashion allowing us to concentrate on concise definitions of the network structure from which the extensional results can be read off
gives rise to the following inter alia verb mor present tense sing mor root sing mor present tense plur mor root plur mor form present mor quot syn form present present mor form passive mor quot syn form passive passive
however this does not generally cause problems since such gratuitously detailed paths being unspecified will always take their value from the most specific path that is specified effectively gratuitous detail is ignored
must be solved to determine the meaning of also2 only read the lellers that sue e sent to paul
here m e there for clarity only and have no theoreti al imporl
for our purpose an iinportant characteristic o krifka s approach is the tight syntax semantic interaction it presupposes
to set the stage for this paper we will briefly review the folklore i.e. the main issues of focus theory
second the prosodic data is rather unclear the assumption that quasi soe contains a prosodically marked focus is a moot point cf
this procedure takes an earley state i kx as input and produces the viterbi parse for the substring between k and i as output
table NUM evaluation results with varying number of features with NUM training sentences precision pr
even then the training size will compare favorably with the huge number of training sentences necessary for many statistical systems
value for the given parse state often using additional background knowledge such as NUM
note that the top of stack is at the right end for the parse stack
the parse stack and the input list contain trees of frames of words or phrases
additional information includes irregular forms and grammatical gender etc in the german lexicon
NUM NUM NUM NUM NUM training sentences with all NUM features and hybrid decision structure train
labeled precision has the strongest correlation with both the syntactic and semantic translation evaluation grades
most concepts representing words are at a fairly shallow level of the kb e.g.
to express such a wide range of features we defined a feature language
this concept is found in the sfm too
figure NUM a fragment of a discourse structure
so how does genedis know which to choose
the generation of the discourse structure of monologues and dialogues are usually treated as separate research paradigms but in this paper we have shown how the gap between the two can be bridged
this part of the network is nonrecursive but as we now re enter the network to generate the acts that will fill the n and s of the move we meet a recursive system network
but given the richness and flexibility of the descriptive and implementational tools available to us it is an enterprise on which we can enter with an expectation of some measure of success
we are attempting to derive disambiguation information by examining the prepositions as given in the subcategorization frames of verbs and in the example sentences in ldoce
instead we will concentrate on a question that we c msider to be of equal importance but that has received surprisingly little attention under what conditions should an i r be applicable to a given lexical entry henceforth le
for example one of the homographs of bank means roughly things piled up the different senses distinguishing exactly what is piled up
as such corpora are hard to obtain usually requiring expensive hand tagging research in this area has concentrated on other forms of lexical ambiguities eg
we found that this new function lead to a small improvement in the results of the disambiguation however we do not believe this to be statistically significant
we show the homograph and sense numbers from ldoce with the stemmed content words from the dictionary definitions which are used to calculate the overlap following the dash
such l rot lems of overgeneration or spurious ambiguity do not arise if a lexical rule al plies to a given lexical ent ry iff the lexical entry is subsumed by the left hand side of the lexical rule
for example company names are usually capitalized and often end with co corp
the generally lower performance levels for the product spotl er is prol ably due to the
in all cases x NUM y remains within NUM NUM interval
the method is being continuously refined as we gain more feedback from empirical tests across several different applications
our intention is that the user s placement in slalom will help guide the kind of constructions that the system should use in its generated text
for instance it is possible that the specific features of the student s l NUM will affect the rate or order of acquisition of the l2
for example english requires a subject verb agreement marking s on most verbs in the present tense when the subject is third person singular
in this way the syntactic constructions generated by the system will provide understandable and positive exemplars of the language features currently being acquired by the leamer
l i in other words at alt branches in the grammar the alternatives will be ordered depending on the user s current placement within sla lom
in particular our current research is focusing on identifying the precise hierarchies orderings and syntactic features in the hierarchies as well as relationships among the hierarchies
thus in some sense both instances could occur because the writer does not understand what form of the verb is required in the given circumstances
it should be pointed out so that it can be corrected but tutorial dialogue on appropriate verb morphology is certainly not necessary and would be inappropriate
in pie the coreference assertions are proposed by the following knowledge sources assumptions about proper names identical proper nouns in a text refer to the same entity
once the dependency relationships of a sentence are established a small number rules can be used to extrac t information that can be expressed in a large number of variations
the coreference module correctly determined that he in NUM refers to dooner and generated two succession events for the chairman and ceo positions o f mccann erickson
pie failed to infer the location of coke from the phrase from coke headquarters in atlanta because atlanta is not a dependent of coke
an evaluation of principar with the susanne corpus NUM shows that it is able to correctly identif y dependency relationships for about NUM of the words NUM
a sgml markup generator that takes a set of tuples low high type contents and generates sgml markups surrounding the words with indices from low to high
the static rule created NUM entries in the post holder database according to NUM NUM post chief executive officer org mccann erickson holder robert l
for each distinct pairs of post and org fields if there exists a record with status in or status out a succession frame is filled
table NUM the performance of pie system in muc NUM we have also included the timing data in table NUM even though speed is not an evaluation criterion
the following table shows equivalent classes and constraints after each assertion this representation of discourse entities does not determine or restrict how the equivalence and differenc e relationships are proposed
backtrack points correspond to conflict sets containing more than one element
assume for instance that b21 generates a second solution
categoryi the categories can be defined as in a context free grammar
analyzing and possibly modifying the tgl grammar used
NUM matching select all rules carrying the current category
g oft ddplafons nous le carrd
f2 est ce que nous ddplafons le carrd
h it encumbers the window which is here the microsemantic structures of the main and the relative clauses are however kept distinct to respect the principle of coherence
r primed words outputs i qiaigh ac t q ow aie t lio w
if the case z corresponds to an optional arg tltllcllt of i or if the latter should fulfill c thanks to a preposition
average robustness of the lfg and the microsemantic
these results show the benefits of our approach
table NUM number of parallel hypothetic structuresl according to utterances length at first we proposed to reduce this perplexity through a cooperation between the microsemantic analyzer and a lfg parser antoine NUM
of i hc ot lwr preposition slots
t o utilize as many t raining data as possible
as illustrative examples consider tile following sentences
we defined an artifmal class based model and genera ted
l the hm rtfing algoril hul
but surface negations must lie inter
this base is divided into worlds
NUM NUM origin of tim mo lel
thus there is typically at least one error in semantic assignment in each sentence and an error in syntactic assignment in one of every two sentences
furthermore carrying over to the source treebank environ ment question types that seem helpful when asked about atr parses will not be di cult
with to stuff the interpretation under which leeslion is a direct object admits an optional p i eotnplement NUM while the interpretation under u hich iocatum is a direct argument admits an obligatory one NUM
this yielded NUM sentences in which at least one of the target adjectives co occurs with one of its antonyms with both the target and its antonym modifying instances of the same noun that are in separate phrases
similarly old means former in the sentence i know that the old family doctor dr schlomm always told manya she could be stabilized on medication that she could be kept under control
it was only to be expected that the lords and ladies of the court would compare the first wife and the second the old empress and the new all in favor of the old
we allow context free productions of the form shown in NUM where a and b are nonterminals and w y are possibly empty sequences of terminals and nonterminals b possibly occurring among 4ollce we admit interpretability by the processor we in principle have tm power
in the first instance we take c to be a signal to the processor to generate an expectation for a duplicate of the terminal sequence that the nonterminal it is attached to gets rewritten to and that this expectation must be satisfied by the next nonterminal of the same name and in the same local domain
we have introduced a new algorithm for the runtime optimization step in statistical machine translation systems whose polynomial time complexity addresses one of the primary obstacles to practicality facing statistical mt
the remaining descendants in the optimal parse tree are then given recursively for any q s t y z by a probabilistic optimization problem
although it is not mentioned in their paper the time complexity for id lp parsing rises exponentially with the length of production right hand sides due to the number of permutations
this is not relevant to the btg based model we have described since its grammar size is fixed in fact the btg s minimal grammar size has been an important advantage over more linguistically motivated itg based models
thus for the normal form btg we have the translation lexicon is encoded in productions of for all x y lexical translations for all x chinese vocabulary for all y english vocabulary the third kind
a btg rather than itg is used since as we discussed earlier pure channel translation models operate without explicit grammars providing no constituent categories around which a more sophisticated itg could be structured
another part of the parameters urpose is subsumed by the sbtg s probabilities at and a0 which can be set to prefer straight or inverted orientation depending on the language pair
in principle it can be used as the translation channel model by normalizing with pr e and integrating out pr q to give pr cle in equation NUM
we hinl ed earlier that we h el there to lm sutlicient reason to believe that copy checldng is a basic cognitive flmction and although we do n t suppose that people have built in production systems and processors isolnorphic to ollr chart parser aim base language we do think that t his copy dmeking is invoked in the processing of crossed depe ildencies
a horse into a trailer rather than transporting by trailer
for example if beggar is treated as derived by the agentive er rule which
the frequency with which a given word form is associated with a particular lexical entry i.e.
we are more interested in incorporating probabilities in a large reusable lexical knowledge base
as expected some very frequent nouns such as car and vehicle had no corresponding verbs
the other was that more specific lexical rules should be preferred over more general ones
another problem mentioned above is the need to ensure that classes have comparable frequency distributions
we discuss how the necessary probabilities and estimates of lexical rule productivity may be acquired from corpora
freq lexical entry with word form prob lexical entry word form freq word form
for instance it is not valid to conclude that because a language is in a particular language class all subsets of that language are also included that language class e.g.
we give now fig NUM a slightly simplified nkrl represeutation of the narrative sentence we have to make orange juice which according to ii i i i fig NUM illustrates the standard nkrl way of representing the wishes desires intention domain
to translate the idea of acting in order to obtain a given result we use i an occurrence here c7 instance of a basic template pertaining to the beiiave branch of the h temp hierarchy and corresponding to the general meaniug of focusing on a result
we introduce firstly the general architecture of nkrl and we give some examples of its characteristic features
for exmnple the key inference mechanism for the factual component is the filtering and unification module fum
the basic inference mechanisms call then be used as building blocks for implementing all sort of high level inference procedures
we computational linguistics volume NUM number NUM sketch the fundamental relations as well as the important tradeoffs between the two frameworks
the problem of capturing more complex distributional constraints in natural language is clearly important but well beyond the scope of this article
to this end we compute the extended left corner relation rlt indicating which terminals can appear as left corners of which nonterminals
computation of p x c for all x can be cast as a system of non linear equations as follows
therefore recursive prediction and completion at each position have to terminate eventually and the parser can proceed to the next input via scanning
how should the parameters e.g. rule probabilities in g be chosen to maximize the probability over a training set of strings
it is also convenient as it leaves the basic parser operations including the left to right processing and the probabilistic computations unchanged
notice this will not only omit certain parses but will also underestimate the forward and inner probabilities of the derivations that remain
when using lexical likelihood we use a lexical likelihood value calculated fl om three word probabilities provided that it is not NUM otherwise we use a lexical likelihood value calculated from two word probabilities
the lexical likelihood values ptc NUM of the two interpretations were calculated as NUM and the lexical likelihood values pt x2 of the two interpretations were calculated as NUM as well
although the phenomena governed by these principles vary from language to language the principles themselves we think are language independent and thus can be regarded as fundamental principles of human communication
further suppose that categories r1 r2 rk are combined into category a and NUM NUM lk are the lengths of r1 r2 rk respectively
NUM furthermore the syntactic likelihood of an interpretation is defined as the geometric mean of the length probabilities of the attachments in the syntactic tree of the interpretation assuming that the attachments are mutually independent
table NUM represents this result as lex3 lex2 x syn when the preference values of all of the interpretations obtained are calculated as NUM we rank the interpretations at random
thus if we think of the transducer as a set of rewrite rules we can now express the context of each rule as a regular expression of natural classes of preceding phonemes
for each pair of states s t in the transducer the algorithm will attempt to merge s with t building a new
the use of alignment information also reduced the learning time the additional cost of calculating alignments is more than compensated for by quicker merging of states
while the learned transducer correctly makes the generalization that flapping occurs after any stressed vowel it does not flap after two stressed vowels in a row
in addition testing each pruning operation against the entire training set is expensive but in the case of synthetic data it gives the best results
in addition the incorrect distribution of output symbols prevents the optimal merging of states during the learning process resulting in large and inaccurate transducers
another example of an unnatural generalization is shown in NUM the final transducer induced by ostia on the three word training set of figure NUM
the root of the tree is the transducer s initial state and each leaf of the tree corresponds to the end of an input sample
this results in machines which may seemingly at random insert or delete sequences of four or five phonemes something which is linguistically implausible
subway topiealized the nearest station topicalized where bequestion null NUM two sentences are mixed in one utterance
also for other japanese and english phrases similar effectiveness in target word selection and structural dsiambiguation has been demonstrated sumita92 b
this approach provides a recovery effect in handling phoneme or syllable sequences and the effect depends on the particular speakers because of individual error characteristics
conventionm mt methods provide multiple translation candidates but no information to use in selecting among them or else just the first possible sentence that is generated
null NUM sufficient speed to avoid to break communication null as an interpreter intervenes between speakers real time response is required to keep smooth turn taking
these numbers are counted from the current tdmt system s transfer knowledge and the numbers of examples are token numbers i.e. not including duplications
on the other hand it has turned out that the linguistic distance between source and target languages reflects the variety of target expression patterns in the transfer knowledge
the japanese noun phrase of the form noun no noun using the japanese adnominm particle no is an expression whose meaning is continuous
tim entering t hoory has tit following adwmtagcs
performance of our reel hod improves by NUM NUM
roughly both versions use the following same forward center ranking for japanese
purthermore the effectiveness of her work is not evaluated with real discourses
this indicates that there l o
mtl me lelfl s of zoa o pronouns
distinction for four japanese verbs haru hiku hiraku and kanau table NUM shows examples of classifying intransitive transitive senses by the proposed sense icm codes in which each digit denotes the choice of the branch in the thesaurus
clues to sense classification are found using english verbs and case labels as well as the sense distribution of the japanese case element the author would like to thank prof yuji mat sumoto for his valuable comments on this research
vj in licat s the verb in the japanese sentence pl p denote the japanese ease markers and n l nj denote the japanese ease element nouns
as the result of this classification process the set eg vj is divided into disjoint subsets eg vj cnl fdl eg v2 cek fak
as the result of searching for pairs of an english class and a japanese case class frame with a large association score the wo case the accusative case is preferred as the most effective case for sense classification
case the nominative ease while those of transitive senses are discovered with the japanese case class frames which contain the w0 case the accusative c se and ni ease the dative case
each cluster is represented as a pair of the class c of the english predicates and the class ca of the japanese case element nouns of wo case along with the level of the class in the thesaurus and the example word
given the collection of bilingual surface case structures for v j we introduce the bilingual class class association score for measuring the association of a class ce of english predicates and a class cj of japanese case element nouns for a case marker p
the point is that not all information ultimately expressed by a word has been available in the speaker s mind at the onset of lexicalization
p ychological re sons the sentence is simply too long for a speaker to hold in short term memory all the information to be conveyed
genotype unigram tagging frequency p r p NUM NUM figure NUM presents the fst that corresponds to table NUM and table NUM
besides the modules for pre processing and tokenization the tagger includes a morphological fst and a statistical fst which incorporates linguistic and statistical knowledge
when there is no equivalent for the brown corpus in french how should one build an adequate training corpus which reflects properly lexical probabilities
second word ambiguity may vary widely depending on the particular genre of the text and this could differ from the training corpus
the next section gives examples from french and describes how morphology affects part of speech disambiguation and what types of ambiguities are found in the language
last open class genotypes should be examined by order of frequency since their number is finite they can also be exhaustively covered
the action and rsubactions remaining subaetions values are respectively an action description and a sequence of descriptions of actions to find in order to recognize the action
otherwise pk emmot be satisfied case c so a can not be successflflly ex ecuted and shonht not be recognized
recognizing an action thus requires that its preconditions can be msured or at letust hypothesized to be believed by the agent
an effect of action finishing at t is represented by a fact token t t o rcb
for each most generm unifier NUM satisfying c constraints e o constraints ci of NUM and action el such that
consider for example the folh wing dialogue fragment a please tell ine how to go to the laboratories
furthermore the action specified by achieve p depends on the situation where p is about to be achieved
previous plan parsing methods however are insufficient for dialogue understanding since they do not handle the effects and preconditions of actions
to keep information on those effects in the edge fact tokens with the aend value as their starting time points are used
for lexical disambiguation we assumed so far that the underlying inference machinery operates on the set of consistent information pieces t rovided by the discourse
but it is of course easy to construct more realistic examples where the inconsistency is much more hidden and does not affect the disambiguation
thus we end up in the third class of approaches which provide us with fully expressive languages to represent discourse and ensure tractability by limited inferences
lexical disambiguation is a procedure which works according to the communicative convention to interpret the discourse as consistent as possible if there is a choice
thai all known approaches which are confined to conceptual knowledge nevertheless rtm into probletns is due to an empirically false estimation they do not take into account that humans are able
by requiring on l he other hand the underlying logic to be sound and as far as possible complete we run of course into well known decidability prot lems
although quite useful for some purposes the performance of surface oriented approaches is inherently limited in that their context sensitivity is always locally bounded see e.g.
in order to see whether the requirements of soundness and completeness can be adequately weakend wehave to study the inferences involved in lexical disambiguation more carefully
thus a natural language system whose re solver is based on such an inference system is not very useflfl since an attempt to resolve an ambiguity is not guaranteed to terminate
in order to support the distinction between terminological and assertional knowledge most formalisms of this class provide two different restricted representation languages the terminological language and the assertional language
yy number of brackets in the treebank that were reproduced by a and b
this shows that b is better at teem1 but also generates nmre spnrious brackets
m1 nyxttb m2xyy ny nn vy ny m2 nn
consequently two crossing errors are counted whereas correcting one would imply correcting the other
in the second compilation step we use the definite clause representation of a set of lexical rules i.e. the lexical rule and the frame predicates to compute a finite state automaton representing how the lexical rules interact irrespective of the lexical entries
among the optional constituents only the temporal adjunct would find appropriate material in the gil input structure under theme
there are however some cases in which an abstract can be created by using surface clues to make conjectures as to which portions are the most important without using deep semantic processing
sl s21 NUM NUM NUM NUM s8 every ego set contains one element
in this equation wi is the only variable
this is possible due to the design properties of tgl rules can not irrevocably influence other parts of the solution
definite relations are a convenient way of encoding the interaction of lexical rules as they readily support various program transformations to improve the encoding we show that the definite relations produced by the compiler can be refined by program transformation techniques to increase efficiency
b o lib c w r frame l frame l w tl NUM ch w l t2lz figure NUM definition of the frame predicate for lexical rule NUM
the importance value of a sentence is defined as the number of supporters testers who selected it as an important one divided by the total number of testers
the transducer for the entire tree can be derived by the intersection of the entire set of transducers associated with the leaf nodes
by a similar construction the rule at node NUM for example would be represented as aa aa0 NUM
the weighted rule belonging to the leaf node can then be compiled into a transducer using the weighted rule compilation algorithm referenced in the preceding section
these c c correspondences are however constrained by other rules derived from the tree as we shall see directly
the regular expressions for each branch describe one aspect of the left context right context p or both
the size of the trees range from NUM to NUM leaf nodes with a totm of NUM leaves for the entire forest
lower case words are allowed if they are not longer than three chm acters for nmnes containing and etc
the set of regular expressions used to capture numbers in written form sechsundzwanzig in german is given as an example
for instance when the pattern for dates matches the input march NUM NUM conv is assigned a standardized version i.e.
the value vi may be a distinguished value that has a more important function in disambiguation
whenever an unknown word has more than one parse it is counted under the appropriate group
we consider a token fully disambiguated if it has only one morphological parse remaining after automatic disambiguation
only after all applicable rules are applied to a sentence all tokens are disambiguated in parallel
this approach does not reflect the outcome of matching constraints to the set of morphological parses immediately
we finally conclude with a very brief outline of our investigation into efficient implementations of our approach
NUM the feature itself may be a distinguished feature which has more important function in disambiguation
in this case the weight of the feature is w fi NUM
is reasonable synchronically then the irregular morphology can be stipulated and will override the predicted begger
formal accounts of some classes of exceptions such as preemption by synonomy have been developed e.g.
lexicalist linguistic theories such as hpsg lfg and categorial grammar rely heavily on lexical rules
briscoe and carroll NUM manning NUM could eventually be exploited to give frequency information
bearded was mistakenly tagged as a verb but this did not appear to cause a problem for these experiments
in this paper we have described a possible approach to application independent technique for controlling lexical rule application
in this case the recipient reading will be preferred as the recipient dative rule is more productive
it should be apparent from the frequencies that large corpora are needed to find instances of some words
thus we believe that this approach could provide a linguistically motivated and practical solution to the problem of semi productivity
as noted above the extension of the lc algorithm to a potentially infinite nonterminal domain i.e. complex feature structures is nontrivial
although this method is rather approximate by its nature it works quite well in most cases standard entries do not prevent the parser from building correct or ahnost correct syntss the latter differing from the former in nalnes of relations on certain arcs
as a result the amount of computing is substantially reduced and though the expected total number of fragments remains exponential with respect to the length of the sentence the cases of combinatorial explosion are fairly infrequent NUM in our experiments
however the majority of the missing rules describe links which do not require agreement and so for ahnost all well formed sentences c r c o for all r NUM i.e. they turn out to be unimprovable
then replacing the homonyms of each synts by their graphic representations that is transforming tbem into ordinary words we obtain hypothetical corrected sentences some of them may be identical as different homonyms may be represented by the same graphic words
further development of the corrcctor includes as the first step incorporation of a large morphological dictionary in the experiments the entries of words absent from the morphological dictionary were added to it before running the corrector i.e. a complete dictionary was simulated
actually particular subsets of words to be changed are not considered but instead the bottom up parsing is performed which constructs syntactic subtrees that contain no more than r modified words here r is a parameter which is succesively assigned values l NUM
the process of varying concerns only semantically empty morphological features such as the case of a noun the number gender and person of a finite verb the number gender and case of an adjective or participle and the like
the same NUM sentences with single random distortions gave the following results NUM turned out to bc well formed and were evaluated by the system as correct or quasi correct in NUM cases the initial sentences were reconstructed in NUM cases wrong corrections were proposed NUM cases gave system failure
t lcb egular languages correspond to simple finite state automata regular relations are modelled by finite state transducers
therefore we have to use the contexts li and ri without mapping them
in the following example we replace the empty u2e by l2e
parallel replacement allows multiple replacements to apply simultaneously to the same input without interfering with each other
otherwise checking later for the legitimacy of the adjacent replacements would no longer be possible
the results lre given in figure NUM l h host suite hie
dista nc wj w returns the distance between wl and w2
for example english thesauruses sttch as roger s thesaurus and wordnet NUM are
large t uilding fig NUM numl er of sta tistical l atlt
lescrihes the thesaurus and stal islical da l a used in l his pa lmr
first existing thesauruses have insufficient vocal ularles especially in i nguages other than english
fig NUM rela tionshi t i etweell the uuml er of rela
t rcb lolts t ll ls rcb lit dos ht ljlo
computational linguistic works suggest that linguistic words are not the perfect units for natural language processing
i introduction one important feature of chinese texts is that they are character based not wordbased
in english a sociological word can be defined by the delimitation of blanks in writing
these two cfgs can be used to measure the default translation quality since idioms and collocational phrases are typically translated by patterns with head constraints
where a b x and y are nonterminal symbols with or without head and link constraints and c s are either terminal or nonterminal symbols
we can define the following ordering of patterns in p this gives patterns with which we can use head and link constraints for building target charts and translations
here k is the number of distinct nonterminal symbols in t and n is the size of the input string
although our patterns have no more theoretical descriptive power than cfg they can provide considerably better descriptions of the domain of locality than ordinary cfg rules
let t be a set of translation patterns b be a bilingual corpus and s t be a pair of source and target sentences
it addition to resolvhig gramtna r t rahling prol len ls our trocl atik l ir ivides a tneatis o training non grmmnar based parsi g
dercotlnl instances of rule firings it trainil g data treel a nk pars s and thus NUM o incorrecl ly estiniatc rtth probal ilitics
of all the parses output for a sentence being treebanked only a small subset are appropriate choices given the sentence s meaning in the document in which it occurs
in addition to tile constant dialogue between the treebankers and the atr grammarian lancaster output was sampled periodically at atr hand corrected and sent back to the treebankers
sec ond we supply a process description of the treebank in which we detail the physical and computational mechanisms by which we have created it
the five lancaster treebankers had to undergo extensive training over a long period to understand the manifold devices of the atr grammar expertly enough to make the requisite choices
for a given lig l consider a linear so x derivation so
in fact we mainly have to check two forms of productions see definition NUM
clearly this result is better than chance but far from significant
the homogeneity values are across the diagonal with mean and
the analysis of the corpora has provided several revealing insights
the bigram and trigram cutoffs were both set to zero
an important issue is about the complexity in time and space of dl
ligs can be seen as usual context free grammars cfgs upon which constraints are imposed
since its production set is non empty we have ccc e l
moreover static computations on the initial lig may decrease this practical complexity in avoiding useless computations
in this grammar all its terminal symbols which are productions in l are useful
we have shown that the parses of a lig can be represented by a non ambiguous cfg
the interior structure of a component see fig NUM is layered as far as the communication parts of the software are concerned
these two principles have serious implications for the design of individual tempo uents and the on ph te system
word aril ies an l othe r stil cal gr rizalion i aets we must recognize that the
this is showfi by the vastly inl rior l cl forniah e o i rcb lc control model ft
yes but for each model we must provide a way to keep tr tck of probabilities as we parse
the average number of hypouyms or nhyp c f
for this purpose we have used wordnet
in figure NUM sense2 would be chosen
figure NUM senses of a word in wordnet
figure NUM roughly proceeds its folk ws
table NUM comparison with yarowsky NUM
tile precision attained by our algorithm is higher
in general these alw oaches focus on nouns
the version used in this work is wordnet NUM NUM
m lle unlock the filter bowl
3f effectuer le renouvellement du liquide hydraulique
this constraint enforces the nominalisation of the action
computational linguistics volume NUM number NUM we observe that the same property holds not only for s but for all nonterminals if the grammar has no useless terminals
the start indices are not part of lr items we may therefore use the term item to refer to both lr items and earley states without start indices
during prediction a wildcard to the left of the dot causes the chart to be seeded with dummy states x for each phrasal category x of interest
otherwise if a ends in a nonterminal y let a y a find the viterbi predecessor state jw t for the current state
if the input state is not complete c the result will be a partial parse tree with children missing from the root node
the uniqueness condition b above which is irrelevant to the correctness of a standard earley parser justifies probabilistic counting of paths in lieu of derivations
in order to define the probabilities associated with parser operation on a scfg we need the concept of a path or partial derivation executed by the earley parser
the algorithm described in this article can compute solutions to all four of these problems in a single framework with a number of additional advantages over previously presented isolated solutions
in many cases the grammar has a relatively small number of nonterminals that have productions involving other nonterminals in a left corner or the rhs of a unit production
for each start index the entries are managed as a first in first out queue ensuring that the dependency graph formed by the states is traversed in breadth first order
in the previous parsing literature attention has been drawn mainly to linking as a filter employed to reduce the search space as early as possible in a syntactic analysis
a technique is proposed which is designed specifically for left recursive categories and is based on the generalization of their occurrences in a derivation
since h is now empty the computation terminates returning c3 the results in lemma NUM and lemma NUM can be used to show that in the main program a node n passes the test in the head of the for statement if and only if lhs ri matches c at n
therefore we have achieved a time improvement of a factor of r log t t
observe that mll m21 and m17 m27 all the remaining nodes of c2 are fresh nodes
if n is no longer a node of the rewritten input tree state associates n with the emptyset
this section investigates several improvements of our compilation approach solving the problems mentioned before
whether a hypothesis is sent also depends on other criteria such as its score
the number of readings hypotheses and chart edges only slightly increase here
clearly such a test should be done every time the chart is extended
these are less concerned with wellformedness of the input but rather with output for other components in the overall system
unification based theories of grammar allow to integrate different levels of linguistic descriptions in the common framework of typed feature structures
it is hard to see how this kind of communication can be interleaved with normal parsing activity in efficient ways
on the other hand this reconstructibility poses constraints on how the codescriptive grammar can be split up in subgrammars
assume that such rules were previously controlled t y constraints which are no longer presell
dillicult NUM rcb xl ril tq t heiti witlitiul including tltii y re a t iliiishilis ill otlr t
ain english a fighter meltns NUM oth a t lane and a person however the original jetpturese word sentouki means only a pla ne
null viewpoints are features that distinguish a node from other nodes in the thesaurus and are good lues for estimating the area to which an unknown wor l should be assign d
one o1 our gems is m develop a corpus based thesaurus c msisting i f a c re thesaurus such as isamap and a eorl us that reflects domain knowledge
to identify john smith as the victim of a kidnapping we must recognize that he is the subject of the passive verb kidnapped
the interaction between top down category driven and bottom up lexicon driven processing is illustrated in fig NUM showing also the effects of the two slash extraction mechanisms
he of tim l ossible case suffixes is a troll morl h inducing l lural in a certain lass of nouns with noun paradigm null
subject verb agreement and nominative case assignment is handled via the subj slot which is coreferential with args subj and after argument generation contains the subject of the sentence of
given t y che alt keywor l ranging over the pllrasal and lexical cal egories of l he gr umnar
one was the net that li uf dew loped for english has no suitm le morphologicm conlponent for the rich inflection of german
p r NUM verb the macroplanner of proverb combines hierarchical planning NUM with local organization NUM in a uniform planning framework NUM
we start with the following sequence of pms
actually this documents a textual decision that no matter how subset and set should be instantiated the argument f in subset f g will be replaced by set f
a element a a b f a f note that f a f is later reduced to f predicate grouping applies to the arguments of derive in a similar way
as an instance of the latter the formula
because learning requires that the examples in the training set be partitioned into the different senses and because sense information is not available in the corpus explicitly this approach depends critically on manum sense tagging a laborious and time consuming process that has to be repeated for every word in every language and more likely than not for every topic of discourse or source of information
rhus the mun er n of the random variables is roughly equal to the nunfl er of prepositions in english and less than NUM
experimeut al results demonstrate that using the dependency information when dependency does exist structural disambignation results can be improved
the more that a keyword or ngram deviates from poisson the stronger the dependence on hidden variables and the more useful the keyword or ngram is for discriminating documents on the basis of these hidden dependences
suppose that within documents boycott is distributed by a poisson process but across documents the poisson parameter NUM is allowed to vary from one document to another depending on how much the document is about boycotts
to get a better look at the crucial differences between idf and fin the middle frequency range NUM we selected a set of NUM words for further investigation with NUM f NUM in the NUM ap corpus
the relationship is shown in figure NUM a plot of iog 0fw and idf for NUM words selected from a NUM million word corpus of NUM associated press ap newswire stories d NUM NUM stories
when she was dishing it ui her husband entered t he kitchen and gobbled up the whole pizza
most of the other linguistic items used in solidarity politeness strategies however do not bear propositional content
the linear order of the input must be preserved in all candidate structural descriptions
this candidate is optimal because it only violates fill m the lowest ranked constraint
the algorithm does not have to explicitly consider arbitrary amounts of overparsing however
the locality restriction is really a special case of a more general sufficient condition
level two contains cells which cover input substrings of length two and so on
the dynamic programming dp table is here a three dimensional pyramid shaped data structure
the operation producing the partial description with the highest harmony actually fills the cell
that locality helps processing should he no great surprise to computationalists the computational significance of locality is widely appreciated
first the cells covering only segment il and then i2 are filled
this weakening also takes place in non coordination examples such as kim became wealthy
for a large class of grammars in effect identical operations can be performed off line thereby allowing for more efficient processing
for example asl uses the same sign i.e. lexical item for other and another
the translation time in the top down method is considere d to t e h sely relate l to the nnmber of possibh stru tures while l he translation time in our new method is not direcdy retle ted by this number
for these translations not much difference could be seen between the new bottom up method and the top down method
in fact this solution does not generalize to cases like NUM which indicate that there is no upper bound on the number of antecedentless bare adjuncts which can appear in a bare ellipsis sequence
it provides a uniform computational approach to a wide range of ellipsis phenomena and it has significant advantages over several other approaches to ellipsis which have recently been suggested in the computational and linguistic literature
f subj n john l noun prop o top complete NUM NUM NUM verb fin i i ndet the NUM det def l i
ace subeattll ii phon flowers synhoe head ease acc subeat i l comp dtrs s
eomp dtrs adj dtrs phon ltoo syn ioc head atype too slnbcattll l
in this method several constituent boundary patterns are applied to an input string in a bottom up fashion
these can be assigned to a substring of an input string when a pattern is applied to it
another target expression is selected when a specific example in the transfer knowledge is closest to the input
table NUM ilead words for NUM s substructures passive matched designated head arc pattern head word
for instance the sentence below has many competing structures mainly because of possible combinations within noun sequences
with these indices patterns are retrieved and checked to determine whether each of them can create an arc
first the creates the active arc NUM relevant to the pattern the x
thereibre assuring efficiency in spoken language translation is one of the most crucial tasks in devising such a system
the system s strengths derive from the fastspec based fastus infrastructure and the weaknesses are problems in japanese name recognition that any system must cope with
the parser and combiner phases recognize a name s surrounding linguistic contexts sometimes converting a phrase of one type into a phrase of another type
this claim is made based on an analysis of writing samples NUM collected from a number of schools and organizations for the deaf concentrating on proficient asl signers
an example text from the cortms NUM is shown below
NUM a d6rtde bir konu ma var
tile qbpic tends to be discourse old inlbrmation and the focus disconrsenew
the ordering is actually tile ibpic followed by the focus
discourse old entities that occur in the ipv position are contrastively focused
the first step is to determine presentational focusing of discourse new information
NUM a mary m went to lhe i ookstore
4some languages such as greek and russian treat presentational and contrastive focus differently in word order
the conception of lexical rules as essential generative devices rather than static statements expressing sub regularities is shared in much in llnential work e.g.
many implementations of horizontal relations however fail to generate lexieal entries on a needs driven basis so eliminate neither the problem of lexicon expansion nor that of inefficient parsing
with ll s some lexical entry is considered as basic and all other lexical entries are derived fl om it introducing otherwise unjustified directionality to the grammar
in architectural terms it is simply accidental if fortuitous that lexical rules are often used to relate minimmly different objects they are capable of much more promiscious behavior
in this sense using underspecification defined in the type system is more econolnic than using lexical rules or a static version of underspecification which is defined in the lexicon
these are provided by structure sharing with the si m consti feature of third argument the prepositional phrase the variable arg3
both the english and japanese muc NUM fastus systems used a graphical user interface called grasper for rule definition and recognized tie up relationships among company organizations NUM
llowever both ibm and john remain a eessible in subsequent discourse
what changes is tile context in the discourse intervening between antecedent and anaphoric expression
a sloppy reading resuits whenever there is a center shift involving c1 and c2
fimr transition types with a NUM re e ren e
have not to my knowledge been discussed t reviously in the literature
this allows us to avoid the complex issues involved in representing such backwards conditionals in a dynamic system
finally we allow the possibility that a property might be the discourse center
the authors are indebted with alberto apostolico rao kosaraju fernando pereira and murat saraclar for technical discussions on topics related to this paper
we performed a preliminary ewduation of tile proposed method by using NUM days japanese stock market bulletins and their fnglish abstracts each containing NUM sentences
as the lexicon coverage for other languages expands it is expected that our acquisition techniques will help further in the cross linguistic investigation of the relationship between levin s verb classes and the basic meaning components in the lcs represent ation
cause thing NUM go loc thing NUM toward loc thing NUM at loc thing NUM thing NUM
we see our approach as a first step toward compression of lexical entries in that it allows lexicons to be stored in terms of the more condensed class grid lexeme specifications these can expanded online as needed during sentence processing in the nlp application
collocation and it is placed in a special slot collocations which indicates that the lcs already covers the semantics of the verb and the preposition is an idiosyncratic variation as in learn about know of etc
in our on going example NUM the thematic grid th loc indicates that the theme and the location are both obligatory in english and should be annotated as such in the instantiated lcs
if the student types jack movio el libro de la mesa a la basura jack moved the book froln the table to the trash the system must determine if these two match
we conclude that while human intervention is necessary for the acquisition of class grid information this intervention is virtually eliminated fi om the lcs construction process because of our provision of a lnapping between semantic classes and primitive meaning components
the structural divergence of NUM is a ccomnaodated as follows the marked leaf node i.e. thing NUM in the enter definition is filled directly whereas the marked
in the current example the ed designates the form of the constant planted which in this case is a morphological variant of the lexeme replant r also the rthe constant takes one of several forms including
by modularizing the lexicon we treat each information type separately thus allowing us to vary the degree of dependence on each level so that we can address the question of how much knowledge is necessary for the success of the particular nlp application
the model s hidden parameters can be easily conditioned on information extrinsic to the model providing an easy way to integrate pre existing knowledge such as partof speech dictionaries word order etc
such errors could be overcome by a model that classifies each word token for example using a part of speech tagger instead of assigning the same class to all tokens of a given type
we handevaluated the precision of the link types in our model in the context of the bitext from which the model 4since function words can be identified by table lookup no pos tagger was involved
after initialization the model induction algorithm iterates NUM find a set of links among word tokens in the bitext using the likelihood ratios and the competitive linking algorithm
let n u be the co occurrence frequency of u and v and k be the number of links between tokens of u and v NUM
the most direct way to evaluate the link types in a word level model of translational equivalence is to treat each link type as a candidate translation lexicon entry and to measure precision and recall
partial links are those where one french word resulted from multiple english words but the model only links the french word to one of its english sources
as shown in table NUM the most common kind of error for the word to word model was a missing link whereas the most common error for ibm s model NUM was a wrong link
word co occurrence can be defined in various ways
in such cases the feature based subsumption account requires that the features associated with the predicate conjunction subsume those associated with each predicate conjunct
ilfformatinn about matching letter substrings and their corresponding phoneme substrings in the dictionary entry under consideration is entered into a pronunciation lattice its detailed below
working in the gpsg framework sag et
figure NUM the lcg analysis of 5c
figure NUM the feature structure subsumption analy
naturally all errors remain our own
the rest of the paper is structured as follows
figure NUM the feature structure subsumption analy
the difference has no effect on the selection of zero prollollll s refercllt
as for the types of subjects a subject should be either a user or a machine
therefore we consider each japanese conditionals in terms of volitionality of the verb
NUM h erefore the subject of the malrix clause should be a user
on the other hand the request form and the solicitation form have some constraints
thus one can not adopt a strategy of retaining the n most plausible interpretations in an analysis which is the most widely accepted practice at present
in particular the theory gives no principled way el deciding the orthogral hic neighbors of it novel word which are deemed to intluence its pronunciation whereas a computational model must spccilically or otherwise do so
replacing the arc sum heuristic of the d n model by arc product as in the prod model leads to a considerable increase in performance e.g. from NUM NUM words correct to NUM NUM for the d n NUM test set with webster s database
our expectation is that the error rate will be relatively high for this test set partly because of its larger size but more importantly because the subjects dialect of english is british rp rather that general american i.e. there is a very significant inconsistency with the lexical databases
the part of speech sequences from the test set served as input sentences that were parsed and disambiguated using the subtrees from the training set
thus we derive that on unedited atis data dop obtains very competitive results if not better results than other systems
o eonstraints ci o g NUM and v
notice that good turing does not differentiate among the types that have not been seen the adjusted frequencies of all unseen types are identical
the parse accuracy for the sentences with unknown and unknown category words is with NUM much higher than the NUM of dop2
dop4 puts all lexical categories p o s tags of the sentence words as found in a dictionary in the chart
the method was tested on atis trees obtaining results that to the best of our knowledge are not exceeded by other stochastic parsers
schabes et al NUM weischedel et al NUM briscoe NUM magerman NUM collins NUM
these are sorted in ascending order since comparing two identical documents would produce a g NUM of zero
but the difference of ranks for of NUM for banana NUM NUM
a component action s precondition however can be satisfied by another component action s effect
time map management is used to capture the temporal state changes caused by the effects of actions
figure NUM shows our divisive clustering algorithm s
values of the fl ames in the training data
i o use a cotnbination of an a ut ontat ically
we also tested m1 i thesaurus wordnel t
where isl denotes the input data size and c
values extracted from a corpus step i
and a hand made l hesattrus or disatnbigua l ion purpose
the latter consist of automatically generated entries
in essence the t l is a generate andtest engine
the contextual rules mainly consist of such filter dags
and the nature of the tagsets NUM NUM vs NUM NUM
sometimes two competing readings could not be disambiguated without sernantico pragmatic knowledge
moreover medical sublanguage sometimes deviates considerably from the standard grammar rules
in fact the wordl example can take this default inheritance one step further by inheriting everything not just syn from verb except for the specifically mentioned values wordl
unfortunately by moving mor form from wordl to verb we have introduced a new problem we have specified the present participle as the default value of mor form for all verbs
we show an intuitive ext l ulation of inferen e of gelling tl e most t referm le reading as i llows
where count w is the NUM j number of occurrences of w in the training set
each part of speech is assigned an initial weight NUM NUM for nouns and NUM NUM for verbs
a new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context
for example suit has two senses listed in a dictionary an action in court and suit of clothes
finally we note that as in most corpus based methods supplying additional examples is expected to improve the performance
we describe a method for automatic word sense disambiguation using a text corpus and a machine readable dictionary mrd
4yarowsky subsequently improved that result to NUM NUM using his one sense per discourse constraint
the medicine sense assignment made in the first iteration has been corrected in the following iterations
this model starts to make an utterance plan before a fully determined domain plan has been obtained
by utilizing this relation speakers can distribute the content of a domain action between two ius
but we transform all these types of answers into binary strings
the utterance plan null ner includes NUM action schemata and NUM decomposition methods
actly match the single parse in the treeb ulc for a NUM NUM word test set
the treebank conversion models tag with an accuracy of NUM NUM
this proposal has been accepted by many linguists
constraint c2 was used to zero proimminalize stations in the focus of attention
without prt gmatic constraints the system generated irrelevant and excessively redundant discourses
there remain some cases very few where such sentences seem to denote some real change of the background but then we claim that negation is lexically incorporated and no longer sentential
we summarize our semantic proposal in the section NUM NUM
d structures represent basic lexical properties such as thematic relations
ibday pierre does n t own a ear
vi as the complement of its head
for example one of the newly introduced errors occurred in the fragment creaking in the fog as it had for thirty years
following the above algorithm both prepositional phrases to school and on friday would be deleted resulting in an incorrect antecedent
any database lookup will return an underspecified feature structure representing all feature structures that are compatible with the feature structure passed to the lookup procedure
it is possible however that a larger sample of relevant examples would suggest the best choice to delete or not to delete in the absence of additional information
if it does and a phrase of the same type exists as a sister node to the head verb in the antecedent then the phrase in the antecedent is removed
following are the specific steps to implementing the algorithm NUM check if there are any prepositional phrases or noun phrases that are sister nodes to the antecedent head verb
step NUM would locate the prepositional phrase in the writing of longstreet and hooper and harris as a sister node to the vpe head verb did
in the sentence julie drove to school on friday and laura did on saturday for example the vpe is did and the correct antecedent is drove to school
the prepositional phrase for thirty years in the vpe caused the removal of the phrase in the fog from the antecedent even though the phrases are not parallel in meaning
in this section we give an overview of the process by which nominator identifies and classifies proper names
to disambiguate highly ambiguous variants then we link them to unambiguous ones occurring within the same document
per son is preferred over place as it tends to be the correct choice most of the time
in terms of semantic disambiguation nominator failed to assign an entity type to NUM of the names it identified
in a kind of discourse anaphora other references to the entity take the form of shorter more ambiguous variants
it assigns weak types such as human or fails to assign a type if the available information is not sufficient
the need to identify proper names has two aspects the recognition of known names and the discovery of new names
each of these can be exploited along a continuum from cheaper to computationally and manually more expensive usage
names with low or zero scores are first tested as possible variants of names with high positive scores
ambiguous operators form recursive structures and so the splitting heuristics apply recursively to name sequences until no more splitting conditions hold
figure NUM definite clause representation of lexical rule NUM word c tl figure NUM
the definite clauses representing lexical rule NUM and its frame were already given in figures NUM and NUM
meurers and minnen covariation approach to hpsg lexical rules input output figure NUM the compiler setup
the problem seems to be that there is no notion of sharing just the type of an object
this results in an encoding of exceptions to a lexical rule in the interaction predicate called by the irregular lexical entries
the fine tuning of the automaton representing lexical rule interaction results in a finite state automaton for each lexical entry in the lexicon
the finite state automaton in figure NUM is constructed on the basis of the follow relation of figure NUM
NUM we now have a first complete encoding of the lexical rules and their interaction represented as covariation in lexical entries
we start by translating each lexical rule into a definite clause predicate called the lexical rdle predicate
the reader interested in that language and its precise interpretation can find the relevant details in that paper
further locality is often considered a desirable property of principles in linguistics independent of computational concerns
an obvious one is to only permit underparsings to be added to partial descriptions on the right side
the generalization to context free structures creates several complications all of which are overcome without compromising the core dynamic programming approach
a local constraint is one which can be evaluated strictly on the basis of the information contained within a local region
the other kinds of parsing operations are matched to position grammar productions in which a parent non terminal generates child non terminals
each parsing operation generates a new element of structure and so is associated with a position structure grammar production
a set of such structures must be computed one for each category before filling input dependent dp table cells
because these values are not dependent upon the input base overparsing structures may be computed and stored in advance
thus the number of passes is bounded by a constant for any fixed position structure grammar
if n is the length of the input the algorithm has computational complexity o n3
we conclude this paper with the following remarks
tually well motivated basis for interpreting old right and short
a spatula is also used for lifting light pieces of food
this section describes our investigation of noun based disambiguation and its results
but there is no sudden transition from hard rock to soft
katz principled disambiguation piece of food being baked
this was systematically true for relationship nouns modified by old
the disambiguating rules we used are given in table NUM
even starker contrasts obtain between the present results and those of e.g.
this requires a formulation using either feature combinations or rule ordering
a substantial subset of them indicate the directional sense of right
then rz is applied to c1 yielding c2
we use all definitions introduced in the previous sections
considering the effects of second language learning on generation
NUM NUM tailoring responses to the student s language ability
at first glance transfer may seem sur3
table NUM extraction rates of newspaper arhcles
3p nlszno flab fu NUM itsu
the evaluation of extracts can not be simply defined extracts can not be evaluated without context for objective evaluation measuring the effect e x the time of prrevlewmg may be realistic
the first experiment is summary for NUM NUM newspaper arhcles and NUM monthly market survey report arhcles both texts are m japanese the calculated extrachon rates based on the total number of
table page m3 contents width weight temperature power consumption kva height heat chsmpation frequency dunenston depth
mformatmn remeval electromc mmi and bulletin board services para NUM cortmo telecommumcauon methods have become s nd d
let us consider the interpretations of the ma trix clauses of the sentences with NUM o
the original version of this architecture envisioned only speech in and speech out as the communication media
if the trigram model ix not so good results are not better than the obtained with l igrams ahme
we intend to follow several lines of research applying relaxation to wsd and to wsd p us pos tagging
t tl i2 the probability of tag t2 given that i he previous one is tl
relaxation can deal with more constraints so we added between NUM and NUM hand written constraints depending on the corpus
extending esl imated fi om occurrences in tagged ort or t
this to higher order constraints is possil le but would result in prohibitive comtmt ational costs
each constraint c c cs states a compatibility value c ibr a colnbinalion of pairs variable label
we have lu esented a two phased t arsing nlethod tor hpsg
note that s and s are completely different entities
this section presents a lexical entry automaton la
an la expresses a set of such skeletal parse trees
prefixed by one of head dtr non head dtr and goa ts
figure NUM the sub structures obtained in the pars ing
the definition of the restriction here is given as follows
the model also has the further commendation that it predicts correctly the observed proportion of left branching compounds found in two independently extracted test sets
this is in contrast to previous work on conceptual association where it resulted in little improvement on a task which could already be performed
however for the pattern training scheme an improvement was made to the dependency model producing the highest overall accuracy of NUM
since these are expressed in terms of categories rather than words it is necessary to combine the counts of words to arrive at estimates
the second type uses a window to collect training instances by observing how often a pair of nouns cooccur within some fixed number of words
for three word compounds it suffices to compute the ratio of two probabilities that of a left branching analysis and that of a right branching one
one problem with applying lexical association to noun compounds is the enormous number of parameters required one for every possible pair of nouns
the fact that underspecified feature structures represent informational differences is used when generating clarification questions to generate uniquely referring nps
to overcome these difficulties we propose a multi blackboard architecture that is controlled by a set of expertsystem like rules
modifying the system s behavior requires modification of the program rather than hardcoding and recompiling
the feature path has to obey the well typed conditions as imposed by the type hierarchy
to generate a clarification question to disambiguate an underspecified feature structure a noun phrase for every disjunct is generated
the variable eti text is assigned to the event event textinput and contains the entered text
tara and nara have the same distribution of usage
it is based on pragmatic properties of japanese conditionals
q d nom conic out pol nonpast
table NUM l istribution of use of tara
table h haracteristics of japanese conditionals
the are shows ability or permission
constraint NUM objects user has intention
ferent from the translation ill NUM
there are several interpretations of this utterance the most obvious being that the system presents some kind of information to the reader
based on the dialogue model the system builds up a tree like dialogue history of the ongoing dialogue see section NUM
other possible realizations of a request would be command offer and statement though none of them applies in the given context
finally we have proposed a stratified model that includes all of the relevant kinds of information to guide the selection of tone
here the collaborative work with speech synthesis will provide us with empirical data that can then be used to refine the classification
on the semantic stratum general knowledge about interactions is located described in terms of the negotiation network cf figure NUM
only in the request or inrathe notation x y z gives a path through a system network
systemically they are derived from the speech function network see e.g. NUM and figure NUM
smith l sid t of ibm mounced rmign ioe y figure NUM parse tree for the reduced sentence in example NUM
hence c a c is the number of times that the words a and c occur in the same sentence ignoring their tags
9in fact we also model the set of unary productions u in the tree which are of the form p ca
the denominator in equation NUM is not actually constant for different basenp sequences hut we make this approximation for the sake of efficiency and simplicity
dependencies are labeled by the modifier non terminal lip in both of these cases the parent non terminal vp and finally the head child non terminal vbd
probabilities of basenps in the chart are calculated using NUM while probabilities for other constituents are derived from the dependencies and basenps that they contain
we should emphasise that test data outside of section NUM was used for all development of the model avoiding the danger of implicit training on section NUM
the following two lew j grammar builds on the one discussed in section NUM the following lexieal entry gives the measure NUM morpheums
in npc the domain is the residue b o this type is denoted by NUM NUM and is defined in 5b
our presentation differs from previous proposals a in that it employs prosodic morphology in the analysis of arabic rather than earlier cv accounts
these nodes are marked lbr emphasis by an asterisk in fig NUM
this paper argues that 11ypothesis a should be rejected on empirical grounds
the cei i lcb g restricts topicalized constituents to phrasal categories
for personal passives the accusative object np of a transitive verb e.g.
mints h m to be restricted to xmta in
then the cells covering the first two segments are filled using the entries in the cells covering each of il and is
a single non terminal may dominate an entire subtree in which none of the syllable positions at the leaves of the tree are filled
if we employ mle as criterion in our simulated annealing algorithm it
they strongly depend on dialogue situations
sontewhat improves t he accuracy rei orl ed
further all input segments will be parsed and unfilled positions will be included only as necessary to produce a sequence of balanced structures
these structures exhibit prototypical context free behavior in that margin positions to the left of a peak are balanced with margin positions to the right
we see that to accurately estimate a model the data size required is as large as NUM times the nmnber of parameters
subdeletion is probably the most straightforward of the error categories
not enough material is included from the antecedent NUM cases
improving the results of the vpeal program is an iterative process
in these cases vpeal selected an incorrect head verb for the antecedent
however examples can be constructed for which this algorithm does not account
NUM too much material is included from the antecedent NUM cases
this paper stems from an ongoing research project on verb phrase ellipsis
we have divided the categories into the following categories a incorrect verb NUM cases
here the above algorithm would incorrectly remove the prepositional phrase in the hilton
we repeatedly generated data and obserwed the learning curve nan ely the relationship between the number of dependencies in the estimated model and the data
only two sentences difl rent structures we re produced by the two methods however all of them were incorrect translations
a system dealing with spoken language requires a quick response in order to provide smooth communication between humans or between a human and a computer
according to the regulation of the linguistic levels relations shown in table NUM a marker inserted string is parsed using the constituent boundary patterns
also we have performed a small translationquality experiment on the two pattern application methods with the NUM untrained sentences within the system s vocabulary
the output sentence is generated as a translation result dora the structure for the whole inl ut which is composed of best first substructures
next we will show how the explosion of structural ambiguity is constrained by dealing with the best only substructures based on semantic distance calculations
there were NUM see examples in table NUM out of these NUM verbs such that their ease slots hawe positive dependency that exceeds a certain threshold i.e.
tagit has been integrated into the german lsgram grmnmar for the identification of word constructs occurring in the mini corpus taken as the departure point for the work of the german group consisting of an article on economics taken from the weekly newspaper die zeit
this tagging component is integrated in a large scale grammar development environment and provides direct input to the grammatical analysis component of the system by means of lift rules which convert tagged text into partial linguistic structures
we describe a robust text handling component which can deal with free text in a wide range of formats and can successfully identify a wide range of phenomena including chemical formulae dates numbers and proper nouns
as usual when using real world texts mm y messy details were found including dates and numbers used within t ercentages and as would be expected from the text type within amounts of currency
a small extract from the corpus used for tim english grammar showed a wide range of possible proper noun configurations james sledz racketeer influenced and corrupt organizations sam a call mr
the syntax of these lift rules is the following ts is rule id tag name features f tag content where ld is a linguistic description ld tag name is the name of an sgml tag e.g.
in addition treating such word constrncts as a sire gle unit gives a significant improvement in parsing runtirne since only the string measure is used as a basis for further processing instead of the original sequence of three words
some auk syntax is the assignment operator parentheses are used for grouping NUM is the disjunction operator indicates optionality of the preceding expression means one or more instances of the
therefore a natural extension to tagit was the implementation of an interactive capability for confirming certain tag types such as proper nouns NUM if a proper noun is found then the tagger first does some lookup to limit the number of interactions during the tagging
the recursion is modelled through the realization rules a new layer of structure is added by each re entry to the network
NUM NUM satellite thematized s NUM for s prefer act satellite act for s re enter at discourse unit
as you will see the first is entered from both nucleus act and satellite act
but this feature also leads on to two further simultaneous systems one for rhetorical relations and a second for satellite thematization
one advantage of using probabilities on features in systems is that they can be varied e.g. for different types of genre lcb cp
it is rule NUM NUM on satellite act that determines what the class ef the act filling s will be
notice that the categories that get inserted into the growinu structure may have similar or even identical labels to the features in the network
the answer lies in the upper system and the entry to this is the third effect of choosing with satellitem
the sfm model of discourse structure is both richer and at the same time more traditional than the types of structure built in rst
typically projects in natural language generation focus on one or the other but some researchers are now considering how the two fit together
nirenburg NUM or are statistical e.g.
adequacy conditions which have to be
ifor the translation from german to english e.g.
otherwise we wouht get wrong results
refills ion tree in l NUM
which contains the ambiguous lexical item sehwester
NUM NUM the incompleteness and decidability of lexieally disambiguating inference mechanisms
the genermisation seems to be that ally combination of phrasal categories is ok so long as one of the daughter categories is identical to the mother category 22a b
thus NUM represents l g represents a variable legal use of punctuation adjoining a i hrasal item since it occurs adjacent to the ad n within the np
there are two solutions the initial verbal phrase can be treated either as a sentence with a null subject or as st gerund noun l hrase
np np vp all the verb phrases for this pattern were imperative ones which can legitimately act as sentences NUM
this obserw tion can be NUM counted or l y structur31 complexity since the sl ru tur31 comt lcxity of the i ut ch sentence figure NUM is NUM which is slightly lower tha n lhc structur31 complexity NUM of the correspollding ernl3ii senl tlce
in contrast in tb the extraposition of pp with a mustache increases tile length of the dependency link between man and with by NUM and reduces the length of the dependency link between talk and ago by NUM thus the structural complexity is increased when pe with a mustache is extraposed
when the complement nil of a verb is shifted to the right across an adjunct modifier of the verb the length of the dependency link from the verb to tile head of the np is increased by length the adjunct modifier
i iow certa iu a rc lo t tlmt the mets will wi me hanislll for constraining exliraposiiiioll is ill gently ee h xl i both parsing 3nd g encr3l ioll
in most languages the np modifiers of a word tend to be loser to the word than it s pp rood tiers which in turn tend to be closer to the word than its cp clansal modifiers
of this is that exlii a i osil iolls 3ill ear to be dependent upon ccrt3in ast ects of ontcxts thi lcb t 3re not cn l ured by usual synt wtic fe3t ures
theretbre the structural complexity of the sentence can only be reduced as a result of the extraposition when the np is longer than the adjunct modifier joe sent the book he found in paris to his pal joe sent to his pal the book he found in paris
the notion of structural complexity is defined in section NUM we then justify the definition of structural complexity by demonstrating in sections NUM NUM and NUM that sentences with lower structural complexity are easier to understand than otherwise similar sentences with higher structural complexity
met name recognition with japanese fastus
examples are shown in figure NUM
this ruledictionary trade off must be fully explored to increase name recognition accuracy
the sgml tagged input document is first tokenized
document processing outputs a set of template objects that represent extracted information
the first implementation of the fastspec based japanese fastus is the met system
these examples indicate the fact that ie requires substantial sublexical analysis in japanese
we also plan to make this japanese ie system accessible to english speaking analysts
it was developed from scratch in a NUM staff month effort on internal ir d funding
a detailed analysis on the internal structure of main clauses subordinate clauses verbal clusters clausal topoi e.g.
in case the stem can not have umlautung as for kommst st also attaches
figures are given which prove that the system is not so far from real applications NUM
the relation and tension between these parameters is the topic of this paper
so for illstance the tree l l u is lt inled by forming tile edges hate l peter and hate NUM woman
the way free wu iables are used in our scheme is somewhat remi n i scent of the nse ol vvitlac lic variables he in montague glanlliiar
however thet e exists a mapping fiom s forms to u h rms the scope fi rgetting mapping which permits to deiine equiwtlence chtsses among storms sharing the same u form
every position in the agenda is sequentially checked whether it can be disambi guated or not
in order to preserve the reusability of the dictionary an extra software layer hides the database
the only thing to do is to define the appropriate filters
it must be mentioned as well that word order in medical dutch can be rather free
the dictionary is conceived as a full form dictionary in order to speed up the tagging process
all possible morphological analyses of a word are provided by the database or tile word recogniser cf
all the results were manually examined and synthesised of
the subsequent section NUM is devoted to the evaluation
each section is illustrated by an cxainple or some implementation details
graphunification provides a neat and easy way to impose various restrictions
so given a datr description i.e. a set of definitional statements and an initial node path query we look for the node and path as the left hand side of a definitional statement
what we need is a way of specifying inheritance relative to the the original node path specification whose value we are trying to determine rather than the one we have reached by following inheritance links
we have already seen how values are explicitly stated in this and the following subsections we continue our exposition by providing an informal account of the semantics of specification via inheritance or by default
the intent of this path extension is to allow descriptors to provide not simply a single definition for a path but a whole set of definitions for extensions to that path without losing path information
however to transduce atoms in the path domain to atoms in the value domain see section NUM NUM below it is extremely convenient to use abbreviatory variables over finite sets of atoms
as noted above the passive forms of these subregular verbs will be correct now as well because of the use of a global cross reference to the past participle form in the verb node
if the mood of the form is active and the tr verb node says that anything that is not passive is active then the subcategorization frame is the same as the argument list
within our research team the design of the interlingua ilt was determined by the needs of uniticatkm based parser and generator writers
the second right hand side literal in the original rule leads to the following magic rule magic up p0 p1 csem magi c s p0 p vform ssem vp pl p vform csem ssem
the most studied area in pragmatics has been the illocutionary force of utterances
this section gives a brief overview of our approach to translating spoken language
one type of pragmatic information relates to signaling discourse structure
discernment refers to the speaker s recognition of her relationship with the addressee and the situation
the result of this is the actual utterance that is presented to the listener
in our work we extend this model to cover a sequence of separate sources of distortions
another type of utterance strategy expresses the speaker s attitude towards the propositional content in the utterance
for each substitution the variables of the rules are instantiated and each predicate of the condition is evaluated until either one predicate fails or the condition yields true
morrill specifies polar translation functions which convert lambek types that are marked for position span to labeled linear formulae
alongside such developmenls vmiotts work ha s addressed i he tssoeial e t parsing NUM rol lem
note that the compilation process must also gencrate additional assumptions corresponding to the positive subformulae of the right hand side of a query e.g.
compilal ion of xo y yo z xo z simplifies the right hand side formula to atomic x giving and additional assumption z
we observed above how hypothetical reasoning in a proof is driven by the presence within higher order fornuflae of positively occurring subforinu null lae
the additional assumption generated in compiling a higher order formula such as xo yo z will itself be marked with a unique index
the results for vehicle nouns were manually checked to ensure that the unusual verb
furthermore if broad coverage is attempted the polysemy problem is still acute
we call this representation the lexeme for a given word
one of these was to allow for blocking which is discussed below
we discuss some more elaborate measurements for productivity in section NUM
there are several potential sources for semantically coherent noun classes
recent developments in corpus processing techniques have made this more feasible
this taxonomy only included land vehicles not boats or airplanes
for instance work on word sense disambiguation in corpora e.g.
the problem consists in assigning to each word its correct pos tag and the wordnet file code for its right sense
the method proposed in this paper formalizes the above approach so that the importance of each sentence is calculated as the sum of feature points multiplied by their feature weights
the decision on a tag of a particular word can not be made separately from the other tags
the tagging speed when using transducers is up to five times higher than when using the underlying hmms
first how does the proposed analysis interact with quantification
krifl a s approach defines a rule to rule
NUM NUM two alternative theories of focus rooth s alternative semanti s
translation model precision is a more thorny issue because people disagree about the degree to which context should play a role in judgements of translational equivalence
we will however discuss at the end of the paper other types of lexical differences which may require language specific inputs
to account for this difference we can estimate separate values of x and a for different ranges of n u v
for these applications we have designed a fast algorithm for estimating a partial translation model which accounts for translational equivalence only at the word level
the level at which the model trusts its own judgement can be varied directly by changing the likelihood cutoff in step NUM of the competitive linking algorithm
in the context of the emerging research area o computational semantics topics related to the syntax semantics interfime have deserved special attention
table NUM shows the results of this experimentation where dendroid and independent respectively represent the method of using and not using the knowledge of dependencies
this evaluation criterion carries much practical import because many of the applications mentioned in section NUM depend on accurate broad coverage translation lexicons
one class represented content word links and the other represented function word links NUM link types with negative log likelihood were discarded after each iteration
where the one to one assumption failed but a link type captured part of a correct translation it was judged incomplete
a link type u v was considered correct if u and v ever co occurred as direct translations of each other
our translation model consists of the hidden parameters a and a and likelihood ratios l u v
just as easily we can model links that coincide with entries in a pre existing translation lexicon separately from those that do not
unlike other translation models it can automatically produce dictionary sized translation lexicons and it can do so with over NUM accuracy
we generated links in the same NUM sentences using our two class word to word model and manually evaluated the content word links from both models
recall drops when there is tess training data because the model refuses to make predictions that it can not make with confidence
the fact that this form accounts for NUM of imperatives in the corpus may be seen as evidence for the increasingly user oriented style of instructions for household appliances
software documentation is one domain in which parts of this knowledge base may be derived automatically
projeel we have attcmt ted i address these two issues
researchers in user interface design have started to build tools which produce both code and documentation
these specifications are thus available to be exploited by other systems
this fst examines the syllable structure tape to give harmony marks figure NUM codas cs get a harmony violation mark onsets o and nuclei n are unmarked
l compare the current cf list with the previous sentence s cf list and choose the firs item that is a member of both of the ranked lists the cb
for example we could factor the computation of productivity between subtypes of the input type of a rule and derive more fine grained measures of productivity for each narrow class a rule applies to
NUM correct translation ift can translate s into t do nothing
numeric weights for patterns are extremely useful as means of assigning higher priorities uniformly to user defined patterns
some assumptions about patterns should be reexamined when we extend the definition of patterns
NUM in this method past does not imply the past tense lit a strict sense but rather the sentence is not in the present tense
this representation naturally corrcst onds with a disambiguation task sin w
phen we solve constraint hierarchy wilh required const rmnls
according t o t hc result of lifschil z
we can represent semantic preferences as well
we do this in terms of a translation function r from f structures to udrss
each creation and destruction of a channel is done by interacting with the ils in order to notify the ils of the request and to get back information about the necessary data structures
l he complex tasks performed e.g. by systems with nmltimodal user interfaces or by systems tackling the processing of spontaneous speech often require more than one computer in order to run acceptably last
pvm supplies each message with a tag which simplified the introduction of channels to a large extent roughly a message is tagged uniquely to identify the channel it is sent on
we currently support c c lisp allegro common lisp lucid common lisp and cmsp prolog quintus prolog and sicstus prolog and tcl tk
this is achiewxl NUM y using the standard communication mode of pvm which supports xi i lcb a message passing is done asynchronously
the behavior of l he components allotted by split chammls does not have to be changed since splitting occurs trans i arently for them
channels come in two flavors what on the one hand guarantees succesful comrnunication between any two partners and on the other hand leaves room for tailoring properties of message channels to certain preferences
some of the components u e structured using sul eomi onents that are iml le mented in different programnfing languages and are executed in own l rocesses
we showed that the communication system realized using this methods is advantegeous in several situations and system contexts ranging fi om strictly sequential systems over intermediary forms to highly interactive systems
six of these NUM additional hannels are configured not to use the xi r coding NUM eeause they are used to transfer high volume data e.g.
compared to lexical probabilities they give much more reliable accounts since only NUM genotypes need to be estimated instead of NUM NUM words for lexical probabilities
since the distributions in both cases have a very long tail there are many more words than genotypes for which we can not obtain reliable statistics
the frequencies of unigram bigram and trigram genotypes are computed in order to further refine the disambiguation and results are provided to support our claims
by paying attention to tags only and thus ignoring the words themselves this approach handles new words that have not been seen in the training corpus
as a result if only unigram training data is used the best candidate for that genotype would be jmp occurring NUM out of NUM times
let ft be the number of cases that the tagging is t for all possible taggings t in this example there are NUM possible taggings
if a certain bigram or trigram does not appear in the training corpus the fst will still have a corresponding path but at a higher cost
the input string is represented as a finite state generator and the tagging is obtained through composition with a pipeline of finite state transducers fst s
table NUM an example of biased cost for the unigram sub fst s p r and jmp nmp
mull pie instances of the special sylnbol can be placed on he stack
however in the case of natural languages parsing is of greater interest than mere recognition
if this were the complete story then we could only recognize languages homomorphic to the duplication languages
add a single feature to the grammar interpreted by tile processor as expect a copy
there are any number of ways that this basic notation can be used in a metagrammatical approach
a stronger interpretation could require an expectation for the same constituent analysis of the nonterminal as well
metagrammatical techniques give an alternative that preserve coverage but use special purpose processing
only event nodes can have a subject object or action
in network representations knowledge is gleaned by traversing the graph
a synta ctic representation will have a semantic model
thus mthough they have the same extension they are different intensioually
of course to capture this unambiguously the meaning has to be agreed
l his al s m o of l roscril od or let x rrosl o li nom i ly
the word dog is called the headword of the constituent the dog and dog is an exposed headword when predicting barked topmost headword in the largest constituent that contains it
a viterbi parse for a string x in a grammar g is a left most derivation that assigns maximal probability to x among all possible derivations for x
since string and prefix probabilities are the result of summing derivation probabilities the goal is to compute these sums efficiently by taking advantage of the earley control structure
b the sentence probability p s x is the sum of the probabilities of all complete paths starting with the initial state constrained by x
NUM a grammar is suitable for lr parsing if these transitions can be performed deterministically by considering only the next input and the contents of a shift reduce stack
this probabilistic information can then be used in a pruning version of the earley parser section NUM NUM to arrive at a compromise between robust and expectation driven parsing
unfortunately large scale acquisition of computational lexicons is difficult
illstea l as explained above it is NUM referabh to employ type expansion here letting syn or sem unexpanded so that coreferences are preserved
in fact tile experimental results ill section NUM show that our approach has a ditferent impact on tile syn parser and the sl m parser see figure NUM
for reasons which will become obvious below we will call the first of these parsers the syn parser the second one controlled by the syn parser the sem par ser
clearly the structures i econm snmller however due to the possil le decrease of filter constraints we nmst expect all increase of hypotheses in the parser
this implies that the svnparser should not send its results only when it is completely finished thus forcing the sem parser to wait rcb interactivity is another aspect we had to consider
basically it tells us for each edge in the chart which other edges are spanned the nodes in tile chart correspond to points in time and edges to time intervals spanned
however recta constraints such as of line parsability or lazy type expansion see next section help us to determine those features which actively participate in unification during partial evaluation
as it is cmnbersome to repeal that we are dealing with the agent s belien this shall be taken as read in the rest of this section
the meaning of any node is detined in terms of its relationship with other nodes so ultimately each node is only fully defined by the whole semantic network
existential arcs can be thought of as existentially quantified variables in first order logic fol which are necessarily scoped by some universal
this so tjot desct il 0s some of lhc core aspcot s needed lk r this discussion
the synonym event has no e dct on distributedness or non linearity but affects topological dist mce and deterrninism of search adversely
individual i refers to the concept as a whole and says that it is involved in the relationship specified by the event
e3 in figure l a asserts that two entities fap mer1 and donkey1 are in an beating relationship
it is assumed that it is correctly represented by the fol statement a l o k y y a
these elements are all illustrated in the cornplete syntactic representation of the sentence NUM given in figure NUM notice the attachment of the sentential adverb aujourd hui as adjunct to tp the highest projection of the representation
it is also a step towards integrating the lesign and documentation processes which is now widely recognized as being desirable
let vt and vh be the votes of the lowest and highest scoring parses for a given token
we have also provided the morpheme structure where s indicate elision
disambiguation this section outlines our approach to constraint based morphological disambiguation where constraints vote on matching parses of sequential tokens
one can see from the last three columns of this table the impact of each of the steps
our system is implemented in prolog and we are currently investigating an efficient implementation based on finite state transducers
when a finite state recognizer corresponding to the input sentence which actually may be considered as an identity transducer is composed with a constraint transducer one gets a slightly modified version of the sentence transducer with possibly additional transitions and states where the votes of some of the labels have been appropriately increlnented
this can be implemented again using finite state transducers as described above except that path vote is apportioned equally to relevant parse votes but instead of selecting highest scoring parses one selects the path from the start state to one of the final states where the sum of the parse votes is maximum
denoting a function key in the computer can not be parsed as a turkish root word tokens which are still ambiguous with ambiguity resulting from different root words we discard parses if the frequencies of the root words for those parses are considerably lower than the frequency of the root of the highest scoring parse
apos before they are mapped to umos can be viewed as variables for umos for convenience we continue to refer to them as apos
by instantiating e and p in pattern a to different logical connectives and derivation relations we have alltogether five rules in this category
we argue in this paper that sophisticated microplanning techniques are required even for mathematical proofs in contrast to the belief that mathematical texts are only schematic and mechanical
specified in terms of concepts in an uniform ontological structure called the upper model our semantic aggregation rules are more compact than similar rules reported in the literature
the main tasks of our microplanner include aggregation to remove redundancies insertion of cue words to increase coherence and reference choices as well as lexical choices
in the NUM place case para can be mapped into object leading to the noun parallelism or quality leading to the adjective parallel
we argued in this paper that sophisticated microplanning techniques are required even for mathematical proofs in contrast to the belief that mathematical texts are only schematic and mechanical
the most structured categorization we found is the work of dalianis and hovy NUM where they define aggregation as a way of avoiding redundancy
it reflects the familiar phenomenon that when several derivation steps form a chain they are verbalized in a more connected way
to accommodate the phenomenon of a chain we have also added a slot called next in the domain model concept derive chain
it achieved fairly high accuracy although it is necessary to farther merge the clusters so that exactly one clus ter corresponds to one hand classified sense
where a is a terminal symbol that matches the current input xi add the state i NUM kx a move the dot over the current symbol
the raw instructional corpus t rcb om which we take all the examples we have coded has been collected opportunistically off the internet and from other sources
k greater than zero is not sufficient to draw any conclusion though as it inust be estabfished whether k is significantly different fl om zero
our interest is in finding correlations between features related to the function of a preventative expression and those related to the form of that expression
consider null NUM to make a piercing cut first drill a hole in the waste stock on the interior of the pattern
in nominal scales tiler is no relation between the different categories and classification induces equivalence classes on the set of classified objects
the flmction features which are more subjective in nature engender more disagreenmnt ainong coders as shown by the k vahms in table NUM
according to rietveld and van hout tile awareness feature shows substantial agreement and the intentioimlity feature shows mo lerate agreement
as the names of these values may be slightly misleading we discuss them in detail here con is used to code situations where s expects h to intend to perform
here h is aware of the choice of various cleaning methods but m w choose an inappropri null ate one i.e. scrul bing or wet mopping
figure NUM from left to right top to bottom tree t
figure NUM translation algorithm computing m g for a tts g
every operation executed by the algorithm has been considered in the above analysis
the following lemma provides a characterization of aa that will be used later
if m nr then the two following conditions are equivalent
some of distorted sentences turn out to be well formed for the distortions described the probability of this is about NUM NUM
thus there will always be a default single symbol mapping corresponding to the commonest pronunciation of the letter
this occurred for NUM of the twb words and NUM of the webster s words irrespective of the scoring model
finally we intend to assess the impact of incorl orating inlormation about word frequency in the analogy process
in pba most words are pronounced by retrieving their phonemic form from the readers s lexicon or dictionary
two prioritised heuristics are used to rank the pronunciations and the top ranking candidate selected as the output
all models perform better with the twb database than with webster s probably simply because of its smaller size
the best pronunciation is found by depth lirst search of the lattice implemented as a preorder tree traversal
a correct pronunciation for a given pseudoword was considered to be one produced by any of the subjects
the performance on lexical words temporarily removed from the lexicon has not previously been assessed but seems worthwhile
for example it serves in a similar way to the predicate used for the introduction of a propositional complement of propositional attitude verbs
the solution has a general character several scope taking elements can go into scope relations collectively if they belong to the same semantic class
as mentioned above it is apparent in japanese that a sentence can include a number of discourse relation elements fig NUM
for example multiple occurences of modal expressions show a concerted behavior as regards scopal relations as in we can perhaps meet there
one of the most important factors is lexical determination of the scope domains of the antecedent part or the conclusion part of a discourse relation
the lud formalism that describes drss in an underspecified way also pertains to dealing with multiple discourse relation constructions which are common in japanese
as for discourse relations a major source of complication comes from the assumption that predicates for discourse relations have two holes as their arguments
in the current version the top hole is simply assumed to be the hole argument of the sentence mood predicate of the main clause
the basic idea is that natural language expressions are not directly translated into drss but into a representation that describes a number of drss
in fig NUM an explanation relation in the subordinate conjunction and another one in the modality auxiliary are used together with a topic relation
consider the definition of o command tbr linear obliqueness simplified version xue et al NUM x o commands y iffx is a less oblique coargmnent of z that dominates y
but it will be easy to check that NUM is adequately defined tbr such cases t br whose current analyses the improvements proposed here have no impact
any node in the hierarchy is preceded only by subjects because in each clausal ar s value only subjects can be less oblique than any other argument
as at z pronoun it obeys principle z xue at al NUM nevertheless the authors oflbre t no solution tbr accounting tbr the thct that syntactic ziji is subjectoriented
a maria t alou acerca delei corn o pe dro i maria talked about him to pedro b a maria lhlou cem ele i acerca do pedro
NUM due to space constraints other cases where x and y do not precede each other but one is not as oblique as the other were not discussed in this paper
this is basically a bottom up procedure
firstly a pattern is not necessarily lexicalized
sentence pairs in the corpora however should not be just added as patterns since they are often redundant and such additions contribute to neither acquisition nor refinement of non sentential patterns
the internal representations of these patterns are as follows
over NUM of jeida sentences were correctly translated
having made these decisions the system then jumps to the associated subdialogs and follows its plan to completion
if pure speed is not the primary motivation the incorporation of several modules each l his rescm ch was funded by the federal ministry of l dncat ion science ll esem ch and technology iimbf in the framework of the vi hiimobil project raider granl s NUM iv 10l a o and NUM iv NUM g
the two other kinds of process communication largely available namely shared memory and remote procedure calls are disadvantegous for our purposes the employment of shared memory may lead to memory or bus contention when several processors are sinmltaneously attached to the same physical memory segment
in l his file br each endpoint a list of real chaimels is defined e mh of which points to a compolmnt and is equipped with a name onfiguration flags and its purpose whieh can be sending or receiving
because the communication is built u NUM i y strictly using the featm es of ice and the underh ying pvm the apl licatiott cnn run on single host s well as distributed to the hosts of a a local area network
in the experiments the algorithm disambiguated our texts about NUM NUM words long of semcor a subset of the brown corpus
several values were ified for the parameter and it was found that the best lmrl ormanee was attained consistently when the parameter was near NUM NUM
tim sense o1 w contained in the subhierarchy with highest conceptual l ensity will be chosen as the sense disambiguating w in the given context
the density of concepts in the hierarchy concepts in a dense part of the hierarchy are relatively closer than those in a more sparse region
in wordnet the NUM senses of i t s related to music appear in the following files artifact attribute communication and person
il the sense level is unable to distinguish among two senses the file level also fails even if both senses were fronl the same file
figure NUM precision and coverage all the data for the best window size can be seen in table NUM the precision and coverage shown in all the
for instance wordnet lacks eross categorial semantic relations which could he very useful to extend the notion of conceptual density of nouns to conceptual density of words
to illustrate how conceptual NUM ensity can help to disambiguate a word in figure i lhe word w has four senses and several context words
figure NUM shows that as expected file level matches attain better performance NUM NUM overall and NUM NUM for polysemic nouns than sense level matches
the edge current pos is used for storing the position of the current token in the variable pos and the edge seek is used for calling the fst named name where var is used as a storage for the output of name
the specification of the current data has been performed on a tagged corpora of about NUM texts ranging in size from a third to one page which are about event announcement appointment scheduling and business news following a bottom up grammar development approach
in order to measure the coverage of the fragment combination patterns fcp the relevant main verbs of the tagged corpora have been associated with the corresponding fcp e.g. the fcp for transitive verbs without changing the original definition of the fcps
however from the NUM of unrecognized words about NUM are proper names which we will handle without a lexicon and NUM NUM are spelling errors so that the lexicon actually covers more then NUM of this unseen text corpus
more precisely a basic edge is a tuple of the form name test variable where name is the name of the edge test is a predicate and variable holds the current token tc if test applied on tc holds
first experiments using a training set of NUM NUM words and a set of about NUM learned filter rules yields a tagging accuracy including tagging of unknown words of NUM NUM NUM note that the un supervised tagger required no hand tagged corpora and considered unknown words
a reading is a triple of the form stem inflection pos where stem is a string or a list of strings in the case of compounds inflection is the inflectional information and pos is the part of speech
applying regular expressions the text scanner is implemented in lex the well known unix tool the text scanner identifies some text structure e.g. paragraphs indentations word number date and time tokens e g NUM NUM NUM
the prefix matching mechanism is used in conjunction with the kleene star and the identity edge var to allow for searching the whole input stream for extracting all matching expressions of an fst e.g. extracting all np s or time expressions
general form of fragment combination patterns a fcp consists of a unique name an recognition part applied on the left input part and one for the right input part an output description part and a set of constraints on the type and number of collected fragments
the limited space available here allows us to provide only a small number of examples shown in figure NUM we see that the use of modals is excluded in the expression of function result and constraint whereas goal and substep do admit modals
drawing too fine distinctions in the corpus analysis at this point in the absence of a test for assigning a unit to one of these constraint types would have rendered the results of the analysis more subjective and thus less reliable
we need therefore to characterise the linguistic expressions of the different elements of the task model and to establish whether these expressions are sensitive or not to their context that is the functional section in which they appear
the macwrite manual is organised into three chapters corresponding to the three different sections identified earlier a tutorial a series of step by step instructions for the major word processing tasks and a ready reference summary of the commands
we omitted the tutorial because the generation of such text is not our concern retaining the other two chapters which provide the user with generic instructions for performing relevant tasks and descriptions of the commands available within macwrite
thus for trivial recognition tim string duplication languages are easier to process than the string reversal lazlguagcs
dices w denotes a sequence of elements of terminals and nonterminals a b denote nonterminals
this is a concrete illustration that not every language costs the worst case recognition complexity for its expressivity class
this method also preserves a relative difference between parsing ww and ww n at least for ps3
in the following section we consider the kinds of flexibility which this component should ideally provide
however other parameters particularly those involving user freedom and system goals require further investigation
corresponding to expressivity class and the associated model of computation is the complexity of recognition for each class
within the same set of restrictions the implemented constraint could have been expect a reversed copy
v so is there any other object which is particularly relevant to this one
ilex NUM is still far from generating discourse of the sophistication shown by a real museum curator
in a sense meeting the challenge subverts the very idea of hypertext as we know it
a central attraction of the hypertext interface is its suitability for the task of information browsing
in this trivial recognition model we could take tile serial ordering as primitive but to use the same model as a recognizer for the context free string reversal languages would require an additional step of reversing the second tlalf of the string before checking equivalence which means the recognition complexity is nlogn
keywords content selection text planning applications hypertext
this paper has discussed several of the parameters of variation in dynamic hypertext syst ems
in a sense the users freedom is an illusion they can not fail
in the sample of sentences containing light the following might be subsumed under such attributes aircraft brigade car cart defense guard horse industry package shell tank and weight
for example short has a sense inadequate that is related historically to its dimensional senses however this sense does not have a lexically specific antonym whereas the dimensional senses do long and tall
family life and master are ambiguous and once the ambiguity is resolved the sense of the modifying computational linguistics volume NUM number NUM adjective is reliably indicated this issue is addressed in section NUM NUM
in the axiom defining chance sign s result state s0 s consequences st and s2 correspond to fin e t and fin e22
a simplified version of this theory is developed in a dynamic semantics framework
the extended morphs is processed by the parser for various values of the parameter r which limits the number of changed words in the sentence
null as a result of morphological analysis for each word all its possible morphological interpretations called homonyms are constructed
at each stage the sentence is looked through from left to right and attempts are made to link each fragment with its left neighbors
so an additional pruning step was introduced which will be described for tile case of synts for fragments everything is quite similar
the repeated experiment with distorted sentences generated by a different series of pseudo random numbers gave respectively the figures NUM NUM NUM and i
although the number of different graphic words in a paradigm is usually less than the total number of forms it is also rather large
these input njodes a re synergistic
it allows the spokeu uttera nce
he inuh i modal nmt hod
design method for building grammar ba sed
it is straightforward to generalize this result for cases with more than two contents and messages a more salient content should be referred to by a lighter message when the combinations between tile contents and the messages are complete
apt li al ion d mains in ore case text i ro ssing is
since for statistical taggers NUM of texts can be disambiguated solely applying lexical probabilities it is in fact tempting to think that with more data and more accurate lexical estimates more text could be better disambiguated
similarly we suggest that genotypes be classified in categories closed class genotypes contain at least one closed class part of speech e.g. des which belongs to the p r preposition article genotype
in the case where two consecutive genotype unigrams do not compose a bigram seen in the training corpus there is no context information that can be applied and only the information of the tagging of the individual unigrams is used
second in their work ambiguity classes can be marked with a preferred tag in order to help disambiguation whereas in our work there is no special annotation since words get disambiguated through the sequential application of the modules
vls v2s v3s genotype frequencies vs lexical frequencies semi closed class genotypes contain only open class parts of speech but behave very similarly to the closed class genotype with respect to the small number of words often homograph in that genotype
the corresponding paths through the fst are shown in figure NUM in the first case bigrams the tagging of p nmp is at a cost of NUM NUM whereas in the
the table shows the genotype in the first column the number of occurrences in the second one the part of speech distribution in the third one the best genotype decision and the percent of this selection in the last column
to make the use of biased cost clear table NUM shows the unigrams p r and jmp nmp that compose the bigram described in table NUM and the corresponding transition costs
the translation of noun phrases np or determiners is quite close to that of the montagovian tradition
the ideas developed in this paragraph are based on a study of negation in discourse currently in progress
2note that the morphological decomposition of the verb shown in 7b will not be
this leads to the complete treatm nt of time sket hed her
we want to address this probh m here with a focus on sentontial negation in french
informatioil such an wentuality a did n t oc ur
in particular it becomes easier to differentiate the semantic contribution of each element that is relevant at the aspectuo temporal level
these elements fall into two categories those below negation and therefore inside its scope and those above negation
we shall not describe this study in detail here nevertheless one of its findings is relevant to our discussion
in one of our projects interlace the target application is just a lisp program running in the same process space as the nlp system so the primitive interface functions ifs for communicating between the two are just lisp function calls
as a result we have not attempted to incorporate any generalized case frame rules into the interpreter itself so tinsel is not bound to any particular theory of thematic relations giving the system developer maximum flexibility in devising useful semantic representations
the ipc code developed for eucalyptus ported immediately to the new application and nautilus s modular architecture allowed speech modeling nlp knowledge base development and if coding to be pursued independently by different team members with a minimum of coordination
model c define each span s score to be the product of all probabilities of links within the span
table NUM shows tile frequency distributions for discourse relations in tile fifteen diak gues
i ecisions al out how much coarsenittg t o lie are o1 great pra t ieal interest b ut t hey lel etm on the training corpus an l tnay l e olnitted from a eonc t tum
we may thus imagine generating a markov sequence of tagged words as before and then independently sense tagging each word with a disjunct a choosing all the disjuncts does not quite specify a parse llowever if the disjuncts are sufficiently specific it specifies at most one parse
he parser is trained on an annol ated corpus no hand written grammar is required
an utterance plan is a sequence of colnmnnieative actions that achieves a communicative goal
we propose a a lexical atfinity mode where words struggle to modify each other b a sense tagging model where words tluctuate randomly in their selectional preferences and e a generative model where the speaker fleshes tit each word s syntactic and concep lcb ual
lndccd tii erl ors ill model i wliich pe l forhled worst across the bo lr l were very frequently arity erl ors where ttie desire of a chihl to ltta h lo a NUM articular parent over
consequently the following utterances are produced u2 musashino sentaa kara wa desune
an action consists of an action description applieal ility constraints and a plan
then six dialogues were taken from the NUM dialogues to use in testing for the open data and the rest of the dialogues NUM dialogues were used to calculate endexpr bigrams
wltn local coneston the total number of the pairs of utterances in a dialogue as shown in table NUM the accuracy of our method was higher than that of the default method
in this case rule r is called critical
also let q0 be a fresh symbol
later we will deal with the general case
ordered trees can also be seen as ground terms
spot has been designed to interface to multiple search engines through an object oriented search engine abstraction layer
we also aimed to provide the users with the full functionality of each of the search engines
if the input string permits such an analysis it will be given this completely faithful description with no resulting constraint violations ensuring that it will be optimal with respect to any ranking
given that the universal constraints meet this criterion the overparsing operations may be repeatedly considered for a given level until none of them increase the harmony of the entries in any of the cells
in principle an unbounded number of overparsing operations could apply and in fact descriptions with arbitrary numbers of unfilled positions are contained in the output space of gen as formally defined
the algorithm proceeds by considering all of the operations that could be used to fill a cell and selecting the one generating the partial description with the highest harmony to actually fill the cell
this paper considers optimality theory grammars where the position structure grammar is context free that is the space of position structures can be described by a formal context free grammar
the two structural constraints are satisfied by descriptions with each v in a peak position surrounded by matched c s in margin positions ccvcc v cvcccvcc etc
the resulting harmony of the partial description created by a parsing operation will be the combina2this partial description is not a single tree but an ordered pair of trees
there the input string is mapped to the grammatical structure that is closest if the input completely matches a structure generated by the grammar that structure is automatically selected
strategy or criterion we should employ for estimating the best model
obtained this way wets NUM NUM c
the model was tested on NUM sentences from the rm task containing NUM word tokens and approximately NUM NUM phonemes
wfsts possess several nice properties that are not shared by trees or handwritten rulesets for that matter
let us assume that this aa is initial in the word in which case we go left
aa aoo NUM uaal NUM tdq aa2 27uq bao2 NUM t jah2 NUM su
we presume a function compile which given a rule returns the wfst computing that rule
let us assume that the segment to the right is z which is alveolar so we go left
but this conjunction is simply the intersection of the entire set of transducers defined for the leaves of the tree
in this case it is a which is unrestricted so we can set that at
we gloss over this point here in order to make the regular expressions somewhat simpler to understand follows
much human effort is needed to port the system to a new domain
a desirable approach is one that maximizes reuse and minimizes human effort
parts of the development system that are executed by hand appear shaded
the test set consisted of NUM new messages from the same corpus
experimental results were obtained by applying the generated trees to test texts
trees typically contained NUM or more nodes
figure NUM shows the working development system
the set of derived features is attached
language specific modules are highlighted with bold borders
as expected the subject is often the cb in the sov sentences
there are also situations where no cb or discourse old topic can be found
temporal adjuncts vs arguments in the som cc language english
postposing plays a backgrounding fnnction in turkish and it is very common
this supports the association of discourse new bcus with the ipv position
the term focus has been used with many different meanings
often speakers will drop only those items that are very salient e.g.
to leave de quitter to be year old d avoir an here the wildcard stands for an np by default
let r be the probability that an arbitrary co occurring pair of word types are mutual translations
this figure was plotted after the model s first iteration over NUM aligned sentence pairs from the
note that the frequencies are plotted on a log scale the bimodality is quite sharp
therefore remove all linked word tokens from their respective texts
plotted on a log scale the bimodality is quite sharp
by back translating the results of semantic analysis evaluation is simplified
one important point here to notice is our transcategorim approach
for example the granularity of semantic representation can be studied
figure NUM partial entry for the concept corpora tion
sentences such as the following would signal problems the book read john
the output surface structures can then be compared to the input text
we address in next section how to bypass this drawback
this will be especially helpful in a multilingual environment where meaning components might be bundled differently
nevertheless the methods described here provide a baseline to which additional information can be added
it is easy to show that the number of target charts for a single source chart increases exponentially if we build target charts simultaneously with source charts
for example a discrete variable with NUM values will often be treated as continuous thus overriding the influence of its natural type
in figure NUM the evolution is emphasized by using the horizontal axis NUM for the years NUM NUM
in general this is considered a problem av d can be corrected by using a different scale logarithmic or split
indeed as can be seen in figure NUM a seemingly good choice can be invalidated when the range of values is extreme
figure NUM schema tableau1 presentation of the variables years NUM companies NUM
figure NUM schema tarte3 proportion of com panies NUM in the distribution of the spending
in the input the main type or class of each variable is specified as well as a list of auxiliary types
second it might be possible to reanalyse adjunction in such a way that avoids the problem altogether
grew and remained wealthy and a republican figure NUM the feature structure subsumption analysis of the ungrammatical NUM
we show that the lcg treatment accounts for constructions that have been recognized as problematic for unification based treatments
NUM kim grew and remained wealthy and a republican
now let c and c be two satisfiable formulae that fix the same set of atomic feature constraints
that an argument must logically imply or be subsumed by the antecedent of the predicate it combines with
first of all the years are presented on the vertical axis thus eliminating the impression of evolution NUM
despite their simplicity the computational complexity of the kinds of feature structure and lcg grammars discussed here is largely unknown
note however that NUM his does not affect the ine hmdsm of role deriva ion
in this article we have first shown that it is sensible and promising to comt ine drt s and set s perspectives on lexieal semanti s
the entities constituting ec the action the transitions and the causation are located in a common time span
ltowever there is no similm sul i ort for inferring from 2a to 2b
direct links to the dr s ret resenl ing the hi airing of the german verb yew
some of the roles of the maximum case frame can be put into the foreground these are said to have cmphasis
the les ril lions i rovided by set are suitable as the basis for fine grained representations
figure NUM llsf and l rotoi ypieal sil uation les ril l ion
the sample rel resent tions giwm in lhis section exploit karat a nd lcb ofideutsc her s
o prefix lcb t rcb defined on a base b is denoted by o b
then the muld modm mei hod wa s iliductively defined ba wd
step NUM corpus analysis the collected corl tls is mta lyzed
w rbj y menu action menu menu it era
such a l redica i e is defined it a task specific ltlal tlef
culates the beginning time trod the end time of a ny level of insta ntia ted
the new mora is filled by the spreading of the adjacent second consonant
in cir umscrip lion we can consider multiple preferable models not nec ssary the single pr rable model
c NUM i stands for vx ii x d p x
example NUM suppose we add the following sen once t o the p rcvious scnl e n ces
the oper d ionm smmmties for hclp is similar lo clp exeet t manipulating a ollstraint hierarchy
shouht be satisfied as much as possible for every a and i and if it is maximally satisfied then the following forinnla
ul t osc the following s hitcii c is added ft
null gil is designed to be a target language suited for deep generation processes
in fig NUM the feedback is represented by dotted line
this means that tile output pairs were limited as much as possible
acknowledgments we would like to thank dr
michiharu nakamura dr testuo yokoyama and dr
NUM NUM selection of pairs of words with high
the following are directions for further improvement
table NUM experimental profile and results
to reduce processing time we calculate
these measures are defined as follows
it allows a net of unix workstations to behave like a single large parallel computer
the backoff parameter was not optimized
because we were interested in the role of prepositions in the signatures we also ran the experiment with two different parse types ones that ignored the actual prepositions in the pp s and ones that ignored all information except for the values of the prepositions
itowever they claim that the semantic classification of verbs based on standard machine readable dictionaries e.g. the ldoce is hopeless pursuit since standard dictionaries are simply not equipped to offer this kind of information with consistency and exhaustiveness
in general to figure out her own best strategy s r attempts to infer r s s s strategy by simulating r s s s inference
in a cheap talk game s s preference for r s action must depend on her type for non trivial communication to obtain because otherwise s s message would give no information to r about her type
even if r doubts s s honesty r will try to know what c is because knowing what e is would help r infer what the hidden intent of s may be among others
that is p c is the probability that s intends to communicate semantic content c to r as before m is the set of the messages
in a simple formulation the utility function ux of player x would thus be a real valued function from c x m x c the set of turns
so a meaning game might be a sort of signaling game in which s s type stands for her intending to communicate some semantic content and r s action is to infer some semantic content
note also that the typical instances of meaning game in natural language communication is not like the typical applications of signaling game such as mentioned before even if meaning games are special sort of signaling games
table NUM conceptual rel resentations obtained fl r sentences NUM NUM
at this point we are provided with all chains extracted from the pair of models mi me
we focus in this t aper on the part of tile semantic analyser which deals with semantic eoml osition
the aim of semantic analysis in this context is to build a representation which conforms to a domain model
we focus here only on the strategy for i roducing the set of all possible chains between cl and c2
however it appears that this phenomenon is dependent on the underlying types or sorts under consideration
the output of the semantic analyser is a conceptual graph on which pragmatic inferences are performed to enrich the representation
the reference model of a type represents knowledge about this type as a conceptual graph fig NUM
at this stage we are provided with all the possibles chains between p1 and p2 extracted from their models
of the two paths where last p is joined to first p e is returned
other methods have also been proposed
attempting to model this by static means can thus produce only limited success
discussion of the importance of combining the appropriate domain of locality and synchronization
quantitative evaluation shows the anaphora resolution algorithm described here to run at a rate of NUM NUM accuracy
augmenting the algorithm with a lexical database which includes more detailed gender information will result in improved accuracy
second it is important to note that this is not just a matter of simple comparison
gone was the narrow corner booth that apple shoehorned its products into last year
l he following text segment illust rates the resolution of in tersen ten tia l a napho ra
its dimensions are NUM NUM inches x NUM inches x 2inches
sun s prototype lntemet access device uses a NUM NUM mhz microsparcprocesso and is diskless
these are ranked according to salience weight where the crucial factor is grammatical function value
the fact that these discourse referents are members of the same cop et class
indeed to determine the thematic roles we established a set of thematic rules that affect for a given predicative occurrence its thematic functions according to the predicate type the role type and the argument s semantic class
the same applies for he in NUM however improving our algorithm to process classical cases of cataphors such as that in sentence NUM should not require major modifications only a change in the order in which the ees are searched
eel c rhree of the world s leading advertising groups agence i iavas s a of france young rubicam of the u s and dentsu inc of japan said ee2 they are forming a global advertising joint venture
in the following remember that what we called the basic focusing cycle is the following successive steps applying the resolution rules applying the focusing algorithm i.e. updating the focus registers the evaluation of the proposed antecedents for each anaphor
on the basis of NUM articles of NUM sentences on average containing NUM pronouns altogether we made the following assumption NUM assumption non prr pronouns can have intrasentential antecedents only if these pronouns occur in an embedded sentence
null we will focus on the mechanisms and algorithmic aspects of the resolution how to fill the registers how to structure algorithms etc and not on the rule aspects like how irs decide to choose bill and not john sentence NUM
eel that sam is charming ee2 girls who he has dated say iiobbs also pointed out the cases of picture noun examples as in sentences NUM and NUM NUM john saw a picture of him
around the old holton house he made many improvements
we investigate this possibility introspectively in section NUM NUM
examples of how the semantic representations of a request might look like are given in figure NUM and NUM
in the next section we briefly h scril e tim framework of our method which uses a siml le context model tl n in the following s etions we illustrate its effe tiveness with some actual outl uts of our english to jcl1 anese lna hine translation system
for example if a group of words ending with a cohm is not a complete sentence as in the ease of NUM in figure NUM this allows you to our system adds either do the following or the following t y referring to the tyl e of the next sentence or phrase in the context model
a mechanism needs to be devised that will select among these choices and that will also prune the search to avoid unnecessary computation
in the parsing mode the target syntax is known and a sequence of operators is desired that can account for the syntax
such a segment may be interrupted for the purpose of achieving a new locally discovered subgoal or for approaching a different goal
the system gathers feedback from the user via any means the designer may choose and seeks a set of generation constants that optimize user satisfaction
it receives events generated by other modules and uses timings between output and input events to calculate the user s response time
the prolog like proof tree enables this kind of behavior because the dialogue segments can be built around the explicit subgoals of the proof tree
other individuals who have contributed to the duke programming tutor include curry guinn zheng liang phil long douglas melamed and krislman rajagopalan
traditional analyses of human human dialogue decompose sequences into segments which are locally coherent and which individually address their own subgoals in the overall dialogue structure
when the system pro null rides information to the user it is important that it present the new information at the appropriate level
take as an example the clause representation of 13a and our meaning postulate NUM depicted in 14a b
however mor past participle blocks default definition from mor past
such limitations also stem froin a lack of actual selnantic knowledge
for example although a continuous variable is better represented by a line graph the nature of a discrete variable will become more apparent using a column graph
however if the intention of the writer is to illustrate the enormous difference between company d and the others the graph is very efficient as it is
fortunately when their integration is successful they complement each other very well a picture shows whereas a text describes
this type system classifies the visual and organizational properties of data variables using such categories as nominal ordinal and quantitative
temporal but in the case of text generation it becomes necessary in order to express the units of the variables
after examining a number of reports we noticed that text and graphics were often used together to transmit the same message
if only a single set if goals is specified the system does all the work of grouping and ordering the information
for example line bar column and point graphs can all be used to present NUM variables using positional encoding
the ambiguity stems from the fact that a number of structurally different graphs can express the same variables using the same encodings
this change is important to the perception of the graph because it makes its NUM message clearer by eliminating a false inference
to manually model a NUM word esst vocabulary requires NUM lull days
consequently the ilt design was lot tuned towards connectkmist systeins
feaspar uses a full word form lexicon
fifth a search algorithm is motivated
feaspar is compared with a handmodeled lrparser
generalization performance is increased by sparse connectivity
for the neural networks the average test set performance is NUM NUM
syntactic properties and terminology are NUM
relations may contain more than one element
however after the second step ambiguities in each sentence are kept unresolved in the context model
a 2the text in parentheses in the figure is part of the linguistic context of the task element rather than the element itself
wag displays the results with an indicator of how statistically significant a value is compared to the combined means in the other sets
process types to see how the domain is construed in terms of actions on the part of the user and the software
one large french software house that we contacted does author its documentation in french but had registered considerable customer dissatisfaction with its quality
relations between processes to determine whether textual cohesion was achieved through conjunctives or through relations implicit in the task structure elements
agency to see whether the actor performing or enabling a particular action is clearly identified and whether the reader is explicitly addressed
it is therefore reasonable to expect that at least the same degree of commonality of description is achievable between english and french within this framework
in this event an obvious source to explore is the communicative purpose of the author which is not necessarily constant throughout a manum
while it might be important to separate planned and unplanned effects in the underlying representation we again abstract over them in the lexico grammatical coding
when tested on a common data set our wsd program gives higher classification accuracy than previous work on wsd
however almost all existing work in wsd deals only with disambiguating content words too
local collocation knowledge yields the highest accuracy followed by pos and morphological form
in addition sense definitions are only available for root words in a dictionary
the noun interest occurs in six different senses in this data set
the accuracy of lexas on these two test sets is given in table NUM
the sense of a morphologically inflected content word is the sense of its uninflected form
table NUM shows the distribution of sense tags from the data set that we obtained
in contrast lexas learns from tagged sentences without human engineering of complex rules
in all there are about NUM NUM noun occurrences and about NUM NUM verb occurrences
the lexical categories used in longman are not equal to the lexical categories used in the atis corpus and needed to be converted
this paper is deveh ped along three parts
NUM a di ida si torus si ria
this is done using a tensional control stating whether tile node has an extension in the world an extension in some other franle of existence such as agatha christie s fictional world where tile hamtner was the lnnrder weapon or an unkuown extension
NUM a di ida diri na si john
to identify any such agent would require some form of search which would be inetficient as very often the agent will be l lj l a l istributedness ca n be better exploited by using a control
prep y o pedroi obl y
maria talked to pedro about himself a
what is hard is the carrying out of the program not the program itself
fore requires a determination that the adjective modifies the noun at a deep syntactic level
verbs are therefore useful to the interpretation of adjectives modifying subject or object nouns
in this sentence the head noun pieces is irrelevant essentially empty semantically
the amount of co occurrence data available for inference has had a substantial effect on coverage
this was already done to some extent when proper names were grouped together into classes
determining the potential of this line of evidence is the focus of this paper
there are differences among the different target adjectives in the appropriateness of the feature
somehow it has never been hard for me to believe in francis wounds
however the phrase from light to dark secures the intended sense of light
table NUM success rate without semantic constraints
this work has been funded by lgfg baden wiirttemberg
the functional approach causes only NUM additional errors
in the texts NUM intra sentential anaphors oc
these errors occur whenever the antecedent of an intra sentential anaphor is not bound by the context which is possible but rare and when the anaphor can be resolved at the text level
table NUM distribution of anaphors in the text corpus
st rube coling uni freiburg de
the two panel methodology in the knight experiments
each of these is discussed in turn
the methodology involves four steps NUM
we will call this type of collocation flexible collocation
word chunks are extracted by the word level sorting method
yen the dollar stood NUM NUM yen lower at NUM NUM NUM NUM
the english text was not a literal translation
the method strongly suppresses fractional and unnecessary expressions
one is hased on the full parsing techniques
tyl es would i e useful
in this stage adopt is filled with NUM or NUM each of which represents if or not if a string is subsumed by longer word chnnks respectively
that is when ja lmn us auto trade talks is ardol ted as collocation lapall ils can not bc recognized as a collocal ion though it is i dependently used very often
for example when auto talks between japan and the u s and auto talks between japan and china are two adjacent words the nmnber of coincidences is NUM as in auto talks between japan and
although their apl ro tches a t ta ined high accuracy for the task considered the most crucial knowledge for mt is tnorc coml lex correspolldelices sllch ls ni vp corres oll teltces athi senl et e hwe
this structure would only unify with the line interpretation of gesture resulting in the interpretation in figure NUM
the architecture also supports selection among n best speech recognition results on the basis of the preferred gesture recognition
rules are formed using constants variables functions and predicates together with conjunction and implication connectors
this article describes a grammar based probabilistic parser and presents experimental results for the parser as trained and tested on a large highly varied treebank of unrestricted english text
null table NUM shows the correlation between various parse and translation metrics
the results of the NUM sub tests of each series are then averaged
the output tree generated by the parser can be used for translation
the parse action sequences needed for training the system are acquired interactively
the feature collection is basically independent from the supervised parse action acquisition
figure NUM learning curve for labeled precision in ta ble NUM
when NUM or fewer features are used all of them are syntactic
complex translation entries are preprocessed using the same parser as for normal text
a word gets a primitive frame for each possible par t of speech
inequality assertions are recorded as constraints
both are specified with lisp like expressions
b xyz s president and ceo john smith
the generator guarantees that overlapping markups are nested
the development of muc specific components started on august NUM
equality assertions may cause equivalence classes to be merged
the post is a dependent of the trigger word
the status is determined b y the trigger word
the word named triggers the single rule
each assertion is assigned a weight
hlentifying concepts in natural language text is an important intbrmation extraction task
for each evidence item t its significance weight is computed as
we proceed by examining the lexical context in which tlle seed entities occur
we present and evaluate preliminary results of creating spotters for organizations and products
in the rest of tlle paper we discuss the specifies of our system
various single imrpose spotters have been developed for specific types of conce pts
as defined sw t takes values from NUM to NUM interval
in NUM only the dominating evidence is considered
directly yields two more solutions since the post context of b1 includes via 84j the two elements of v21
the next solution is generated by selecting anyone of the backtrack points and adding a new element to the ego set
for every applied rule the set of c rules applied later in the current subtree of a derivation is stored
NUM lexical covariation encoding lexical rules and their interaction as definite relations having situated the computational approach presented in this paper as a computational treatment of dlrs that emphasizes their domain specific properties we now turn to the compiler that realizes this approach
this lexical rule could be used in a grammar of english to relate past participle forms of verbs to their passive form2 deg the rule takes the index of the least oblique complement of the input and assigns it to the subject of the output
current work in surface realization concentrates on the use of general abstract algorithms that interpret large reversible grammars
the algorithm receives a gil structure as its input and uses a distinguished category txt to start from
the operator denotes concatenation of strings with strings or sets of strings delivering all possible combinations
a figure NUM shows the state of a sample table comprising three backtrack points after all solutions have been computed
by specifically modeling the faithfulness constraints we now allow the fst to have access to the input output correspondences crucial for searching for underlying forms
universes an universe denotes to a couple i i lcb formed of an intension i and an extension NUM lcb
in combining types using just the remaining elimination rule we must still ensure linear use of resources i.e. that no resource may be used inore than once in any deduction and that in any overall deduction every resource has been used
more specifically additional assumptions link to maximal positive subformulae i.e. a subformula y in a context of the form x o y but not in y o z
by providing a met hod of deduction for the implic l ional fragment of linear logic l hal like charl parsing for i sg avoids reeonipul agion of result q i.e.
however we can not simply employ a global binding context since the chart method should be able to return alternative proofs of the same theorem and such alternative proofs will typically induce distinct but internally consistent bindings over string position variables
whenever a formula is added to the datahase a check is made to see if it can combine with formulae ah eady there in which as new formulae are generated which are added to tile agenda
a fllrther crucial feature is that what we derive as all item for any span is purely a function of the results derived for substretches of that span and ultimately of the lexical categories that it dominates assuming a given grammar
for example a positively occuring higher oi der type might have the following pattern of positive and negative subformulae x o y o z consider the following proof involving this type
t i is differing in their resomx e sensitivil y and hence implicitly lcb heir under lying nol ion of qinguisl ie sllllet ure
communication is a game interaction among autonomous agents by definition
though important this point is tangential to the issue addressed here
this subsection discusses why it is necessary to analyze which pragmatic operators were applied to the input and to generate the corresponding pragmatic operators in the output in order to obtain stylistically and pragmatically high quality translations
thus the speech recognizer output which represents the input to the translation engine has traversed four distinct channels or distortion processes each of which is associated with different causes and effects on the message
consequently there is contrary to systems that process data sequentially no restriction that predetermines the point at which some future agent has to perform an action simly because it relies on a specific level of representation
the predicates and functions can tal e variables over either possibly underspecified feature structures over type and feat feature paths over feat or events that enable the communication with the speech recognizer and other external processing modules
our experiments have shown that this method is etfective for inferring the pos of unknown words
we conducted two experiments in each using a range of different thresholds for word measure
the recalls are calculated for ones with the estimated probability more than or equal to NUM NUM
since each element ofp represents a probability the feasible region v is given as follows
next we prol ose a hypothesis which gives foundation to our word extraction method
in this section first we define environment of a string occurring ix a corpus
table NUM recall and precision on ei i corpus
let all elements of left and right probability vectors be NUM
another advantage is that our method is applicable to words belonging to more than one pos
this table tells us that the lower the threshold is the higher the precision is
the second highest domain correlation is with cf commerce finance
extensional statements and combinations of definitional and extensional statements may be similarly abbreviated and the examples used throughout this paper make extensive use of this convention
more generally this mechanism allows us to define nodes differentially by inheritance from default specifications augmented by any nondefault settings associated with the node at hand
if p1 extends several paths defined at n it takes its definition from the most specific i.e. the longest of the paths that it extends
the latter case is easiest there is simply no definition for p1 at n hence n can be a partial function as already noted above
we shall refer to this original specification as the query we are attempting to evaluate and the node and path associated with this query as the global context
this leads us to look for the mor root of word3 which we find at sew giving the result we seek word3 mor form sew en
in so doing we have seen examples of most of the important ingredients of datr local and global descriptors definition by default and evaluable paths
undef is the name of a node that is not defined in the fragment thus ensuring that syn args rest first syn args rest rest and so forth are all undefined
dag case nominative person third number singular referent np referent
in other words we can substitute a straightforward syntactic constraint on descriptions for the less tractable notion of semantic consistency apparently without significant loss of expressive power
now since a new object has been entered on the semantic level and since there is a blackboard that provides a database access procedure for all objects that are subsumed by obj concrete the object database a database lookup is executed
these formalisms were originally developed for the purpose of parallel corpus annotation with applications for bracketing alignment and segmentation
in the case of a production with straight orientation the right hand side symbols are visited left to right for both the chinese and english streams
a first approach to improving the translation search is to limit the allowed word alignment patterns to those permitted by a btg
no morphological processing has been used to correct the output and until now we have only been testing with a bigram model trained on extremely limited samples
the resulting english vocabulary is approximately NUM NUM words and the chinese vocabulary is approximately NUM NUM words with a many to many translation mapping averaging NUM NUM chinese translations per english word
the approach employs the stochastic bracketing transduction grammar sbtg model we recently introduced to replace earlier word alignment channel models while retaining a bigram language model
to complete the picture we add a bigram model ge lej g ej lej l for the english language model pr e
however in the current implementation only the message is displayed on the screen
where v ci is the contribution of constraint ci to the vote of the rule r a generic constraint has the following form c fl vl f2 v2 NUM fro vm where fi is the name of a morphological feature and vi is one of the possible values for that feature
it is certainly possible that a given token may have nmltiple correct parses usually with the same inflectional features or with inflectional features not ruled out by the syntactic context but one will be the correct parse usually on semantic grounds
setting lower thresholds allows the model to focus on more frequent events and produces a proportionately smaller model
the algorithm allows a sequential rule order
also other rules follow the same principle
disambiguation of morphological analysis in bantu languages
an example of a combined rule
the word katika has four readings
logical analysis and after cg disambiguation
participants of all seminars came from
test results are in table NUM
the third part within quotes is the output string
also numerals are grouped according to the same principle
on the other hand the good thing is that spoken language tend to contain less complex structures than written language
finally the search uses a formal feature structure specification as constraint and outputs the most probable and consistent feature structure
these tables are generated automatically from the training data and can easily be extended by hand for more generality and new words
however forcibly replacing words with certain word classes is too loose an approximation which in practice could seriously degrade disambiguation results
the main problem when using statistical approaches for spoken language processing is the large amounts of data required to train these models
with esst the linguistic feature labeler and chunk relation finder networks are connected only to the semantic microfeatures and to relevant statistical microfeatures
in the following tile three main modules required to produce a chunk parse are described the chunker splits an input sentence into chunks
to train and run feaspar feature structure parser only limited handmodeled knowledge is required chunk parses and a lexicon
null using these two information sources the search finds the feature structure with the highest score under the constraint of being consistent
the constraint forces every instance of a left bracket i to be immediately preceded by tilt corresponding left context li and every instance of li to be immediately folk wed by i ignoring all brackets that are different from i irlbetween and all brackets i included
the most recent version of vpeal contained NUM sub parts for ranking and choosing antecedents
the concept of foreground and background information is based on the idea that in narrative discourse some parts are more essential than others
the net result will be to produce a document ranking order which more closely represents the importance of the documents to a user
a central question is how well the concept can be transferred to a domain which although narrative based diverges into commentary and analysis
terms which are less central to a discourse will on this basis be given lower scores because they occur in low transitivity clauses
h participants high in agency transfer an action more effectively than participants low in agency e.g. he shocked me the price shocked me
in an information retrieval context it means that a transitivity index could influence a decision about the relevance of a document to a query
in the initial implementation transitivity scores will be equal to the number of transitivity features associated with the main clause of each sentence
summaries will be analyzed and assessed by volunteers for coverage in terms of the original text and comprehensibility as a separate text
this model has also been enhanced by a number of linguistic techniques expansion of query document terms according to thesaurus relations synonyms etc
f an affirmative action has greater transitivity than a negative action e.g. he called the boy he did n t call the boy
5nakano s original work used an old version of bgh which contains NUM NUM words
this viewpoint should be taken into account when we construct a thesaurus from scratch
furthermore this effort is repeated when a system is ported to another domain
this process continues at the fringe of the decision tree until no more pruning is possible
figure NUM flapping transducer induced from NUM NUM samples figure i0 the same decision tree after pruning
each pair consisted of an underlying and a surface pronunciation of an individual word of english
for other applications it may be desirable to keep a cross validation set for this purpose
like johnson s their system looks at the underlying and surface realizations of single segments
such a rule seems quite unnatural phonologically and makes for an odd context sensitive rewrite rule
at this point the transducer covers all and only the strings of the training set
the next n symbols of the transduction s output are now marked as having been used
in particular systematic phonological constraints such as syllable structure may rule out the necessary strings
one bias is that underlying phones are often realized as phonetically similar or identical surface phones
b on what basis do we choose among a set of potential candklates
actually it is quite easy to find arguments in favor of this second hypothesis
we will leave this as n exercice lbr the motivated reader ben courage
figures NUM and NUM here below illustrate a reasonable way to plan such a message
we cycle through the same kind of process but at different levels of detail
having completed the description of event NUM we still have to specify even r NUM
both person and event are still unspecified elements hence written in capital letters
should we backtrack try to lind conceptual level cl and word level wio
having built a global plan we ilesh it out with details as soon as this becomes necessary
the method establishes a one to one correspondence between subsets of the lfg and udrs formalisms
english classes are taken from roget s thesaurus and japanese classes fi om bgh s
human activities products and natural objects andnatural phenomena respectively
we introduce as the superordinate subordinate relation of closes
examph NUM j watashi ha uwagi wo kagi ni kaketa
spot has been designed to interface to multiple search engines through an object oriented search engine abstraction layer
we also nimed to provide the users with the full functionality of each of the search engines
null in addition words that are of a foreign origin are often transliterated in a number of different ways
we have developed a multi lingual text search tool that is being enthusiastically embraced by users
a system of multiple fdfs can linearly expand the search rate
multi lingual support however is very difficult to obtain commercially
support multi lingual data internationalized support is fairly easy to obtain commercially for a number of commonly supported languages
support multiple search engines our government users currently use a variety of tools for different purposes
query generation tools that allow users to enter kadafi and find the other possible spellings are designed into spot
this provides users with a single user interface tool to learn while providing them with a choice of search engines
there is a robust effect for which given a reference object and a rotated object in question it takes time linear in the amount of rotation to recognize the objects as copies
same processing complexity class since the identity check occurs after tile parse and only requires linear time but we also have structural information about the sentence as a whole
it is convenient to refer to languages with homomorphismswwr lcb wwriwto e lcb a b rcb rcb ai d lcb wwiw NUM lcb a b rcb rcb as and ww respectively
names of variables ranging over feature structures have to start with the name of their type with a capital letter to distinguish variables from constants
to get closer still to the zfirich dialect we require that the duplication operator be applied at the level of preterminals with complementation to get the pairings of case marked nps and vs
the question is displayed on the screen and the execution halts until some text has been entered either via keyboard or via speech recognizer
since ww can be represented directly within ps2 it can be argued that we should not be required to use the metagrammatical method of parsing it just to keep symmetry with the duplication languages
NUM object where the request to order an item is represented in a feature structure subsumed by speechaclorderobject object obj j our next example is also taken from the map application
to calibrate our discussion we quickly review t h salient terminology from formal langm ge theory and the current undersl anding of dm import tor natural language s
b paths shown in a single out the information that is sufficient to completely disambiguate the underspecified feature structure shown in figure NUM for any of the three disjuncts
the point is just that it s possible to keep a cf or regular grammar and supplement the processor with a string duplication operator which can be invoked at the subsentence level
that is only the repairs accomplished by the same speaker are considered
the experimental results are converted syllables before the repair processing
on the average NUM of turns contain at least one repair
correcting speech repairs make more reliable environments for the subsequent processing
is often accompanied by a disruption in the intonation contour
they have more complex surface forms and should be investigated further
in our coding schema each feature determines a nominal scale on its own
drafter is a technical author ing support tool which generates instructions for graphical interfaces
this lack of interest in the two coinmunities has been in some sense complementary
we then discuss the correlations that exist between the function and the form features
little work however has been tirected at negative imt eratives
we will discuss our schema explaining the reasons behind our choices when necessary
the 2x NUM contingency table from which the intentionality value was derived is shown in table NUM
this binary feature captures whether the agent is aware or unaware that the consequences of are bad
we took all the examples for those forms that returned fewer than NUM examples
this hypothesis however was based on a small corpus and on intuitions
ititherto the field of punctuation has been almost completely ignored within natural language processing with perhaps the single exception of the sentence final full stop period
this test grammar includes eight lexical rules some serve syntactic purposes like the partial vp topicalization lexical rule others are of morphological nature as for example an inflectional lexical rule that relates nonfinite verbs to their finite form
roughly dep eliminates NUM the descendant nodes of the node which apl ears both in syn sem domains and head dtr non head dtr domains and NUM the nodes at peering only in syn sem domains excet t for the node which el pears in s ab fs c or goals domains
head dtr syn counter NUM then this can generate an infinite sequence of signs each of which contains a part counter bar ba r bar l and is not equivalent to any previously generated sign
definition NUM quasi sign n for a given integer n a fcatu e structure s is a q aasi sign n if it has some of tile following four attributes syn sem head dtr non head dtr and does not lave values for the paths head dtr non head dtr
cost of using he and the man respectively r utilities are basically assigned to content message pairs but sometimes it is possible to consider costs of messages irrespective of their contents
we assume u1 u to the effect that he is less complex than the man both phonologically and semantically he is not only shorter than the man but also more importantly less meaningful in the sense that it lacks the connotation of being adult which the man has
a turn of corn munication with an utterance of the man was angry with him consists of the sentence level game mentioned above the two noun phrase level games one concerning the subject np shown in figure NUM and the other the object np of with and so on
ux should then depend on not just ks m and c but also the players nested beliefs 5people have common belief of proposition p when they all believe p thcy all believe that they m1 believe p they all believe that they all believe that they all believe p and so on ad infinitum
when the man was angry with him is used despite the smaller default utility associated with it max will probably be assigned a greater salience than otherwise which is again a sort of accommodation
that is provided that communicating agents play the role of s and r half of the time each they can maximize their expected utility by setting their utility functions to the average of their selfish utilities
our experience suggests that if the number of distorted words is small and they are syntactically isolated i.e. the corresponding nodes are not too close to each other in the synts of the original sentence then the system corrects each distortion independently of the others as if it were the only one in the sentence
therefore the constituent structure of NUM a
this is made evident by NUM a
it is the other way around
now for every inactive chart associated with a nonterminal symbol x for a span of i NUM i j n there exists a set p of patterns with the source cfg skeleton x
practical implementation of mt systems based on these formalisms on the other hand would not be possible without much more efficient parsing and disambiguation algorithms for these formalisms and a method for building a lexicon that is easy even for novices to use
can handle such np pairs as one year and un an and more than two years and plus que deux ans which would have to be covered by a large number of plain cfg rules
some prepositional and sentential attachment ambiguities needs to be resolved on the basis of semantic information and scoping of coordinated structures would have to be determined by using not only collocational patterns but also some measures of balance and similarities among constituents
proof the decision problem l t c l g of two cfls such that l t c l g is solvable iff l t l g is solvable
one way to trivialize this problem is to let t include a pattern with a pair of pure cfg rules for every pattern with head constraints which guarantees that l h l t l g
this may pose a serious problem when a grammar writer wishes to know if there is a specific expression that is only acceptable by using at least one pattern with head constraints for which the answer is no iff l g l t
as a result users are forced to see the same kinds of translation errors over and over again except they in cases where they involve merely adding a missing word or compound to a user dictionary or specifying one of several word to word translations as a correct choice
the interrupted are phrasal templates only and not predicatiw relations
ikehara et al exclude the substrings of the retrieved collocations
in favor of this l hcy leave a large number of nested colloc ations
so they modify the measure of frequency of occurrence to become
the greater this numt er is the better the string is distribute d
we have to consider the number of tilnes it api ears within hmger candidate collocations
an l the greater its value as a andi late collocat ion
on balance however the NUM million words of email training data look somewhat inadequate compared to the NUM million words used for the wsj lm
this allowed the comparative evaluation of the contribution of vocabulary vs n grams to the lm effectiveness measured using both perplexity and word error rate
the problem arises as a result of the fact that these solutions lead to identical filters for the evaluation of the cat literal i.e. the solutions to cat NUM do not uniquely determine cat NUM
to which extent it is useful to collapse magic predicates using unfolding depends on whether the grammar has been optimized through reordering the 4in addition to the conventions already described regarding figure NUM indices are abbreviated
to this end we have considered data which is viewed as a test bed for focus theory and shown that whilst existing theories either under generate over generate or are methodologically unsatist ctory the ttou approach yields a simple and transparent analysis
with this grammar a simple top down generation strategy does not terminate as a result of the head recursion in rule NUM it is necessary to use memoization extended with an abstraction function and a subsumption check
this is the rule that is derived from the head recursive vp rule when the partially specified subcategorization list is considered as filtering information cf fn NUM
given a logic program magic produces a new program in which the filtering as normally resulting from top down evaluation is explicitly characterized through so called url http www sfs nphil uni tuebingen
even though these approaches often accomplish considerable improvements with respect to efficiency or termination behavior it remains unclear how these optimizations relate to each other and what comprises the logic behind these specialized forms of filtering
in this paper i show how starting from a definite clause characterization of filtering derived automatically from a logic grammar using magic compilation filter optimizations can be performed in a processor independent and logically clean fashion
NUM if the feature fi refers to the stem of a derived form and the value part of the feature con null straint is a full fledged constraint c on the stem structure the weight of the feature constraint is found by recursively computing the vote of c and scaling the resulting value by a factor NUM in our current system to improve its specificity
we argue thai tie expressivity requirements of the corresponding formal languages do not actually entail hat processing reduplication languages require the worst ease time complexity for lmiguages of the same expressive class
the language defined by such a union is no longer psi but will not contain arbitrary ps1 NUM strings and if i NUM then the union will not even contain arbitrary context fi ee strings
number of unattested entries word form unseen pr mass word form freq word form q number of unattested entries word form est freq lex entryi with word formj unseen pr mass word formj x prod lri e prod lrl prod ira
it is also not essential to our approach that these rules be treated as distinct from the viewpoint of their representation as typed feature structures since it would be possible to attach probabilities to subrules which only differed in the semantic type of their input
we assumed that a unique conversion rule applied to each noun and calculated the productivities of the lexical rules as the ratio of the number of words for which verbs were found over the total number of words in the class which were found in the corpus
this could matter if there were competing lexical rules defined on different but overlapping classes and if one class has a very high percentage of low frequency words compared to the other the estimate of its productivity will tend to be lower
the general claim we make here is that if we assume that speakers choose well attested high frequency forms to realize particular senses and listeners choose well attested high frequency senses when faced with ambiguity then much of the semi productivity of lexical rules is predicted
manual checking of the rare derived forms is not particularly time consuming so a semi automatic approach where high frequency forms which are found in an mrd are assumed to be genuine but where low frequency examples are manually checked should be adequate
however we will assume the simple approach here since acquiring the average probability of lexical rule output raises some additional difficulties and we currently have no evidence that the more complex approach is justified given that our main aim is to rank unseen senses by plausibility
it is necessary to allow for the possibility of unblocking because of examples such as the following NUM a there were five thousand extremely loud people on the floor eager to tear into roast cow with both hands and wash it down with bourbon whiskey
for these unseen vowels which consisted of the rounded diphthongs oy and ow with secondary stress the transducers incorrectly returns to state NUM in this case we wish the algorithm to make the generalization that the rule applies after all stressed vowels
NUM NUM t n s NUM t o n vocalic stress the effects of adding decision tress at each state of the machine for the composition of t insertion t deletion and flapping are shown in figure NUM
in a transducer based formalism generalizations about similar context phonemes naturally follow from generalizations about individual phonemes behavior as the context is represented by the current state of the machine which in turn depends on the behavior of the machine on the previous phonemes
we believe that further and more integrated uses of phonological naturalness such as generalizing across similar phenomena at different states of the transducer interleaving the merging of states and generalization of transitions and adding memory to the model of transduction could help even more
figure NUM shows the transducer induced from glish flapping as can be seen from figure NUM the use of alignment information in creating the initial tree transducer dramatically decreases the number of states in the learned transducer as well as the error performance on test data
using phonetic features to build a decision tree guarantees that each leaf of the tree represents a natural class of phonemes that is a set of phonemes that can be described by specifying values for some subset of the phonetic features
note that if the underlying phone is an unstressed vowel cons stress the machine outputs a flap followed by the underlying vowel otherwise it outputs a t followed by the underlying phone
similarly if the ostia algorithm is training on cases of flapping in which the preceding environment is every stressed vowel but one the algorithm has no way of knowing that it can generalize the environment to all stressed vowels
due to space limitations we can not describe the parsing algorithm in detail and give only a sketch
the standard entries contain generalized in formation which is typical of words of the specified categories
compilation of a large syntactic dictionary is a more labour consuming task as its entries contain more complex information
NUM the present corrcvtor does not contain any negative information intended specifically for correcting errors
the extended morphs is the union of the sets of variants built for all homonyms of the initial morphs
a word of the sentence was chosen at random for which the number of homonym s in the extended morphs was greater than that in the initial one the mean number of such changeable words in a sentence was NUM NUM while the mean length of a sentence was NUM NUM words
if two fragments are adjacent in the sentence then drawing a syntactic link from a certain node of one fragment to the root of the other creates a new fragment on the union of segments occupied by the initial fragments this is similar to constructing a new constituent from two adjacent constituents
on average the extended morphs is much larger than the initial for NUM sentences from the computer science abstracts the mean number of homonyms in the initial morphs was NUM NUM n while in the extended one it was NUM NUM n where n is the number of words in the sentence
the occurrence of exactly one bracket pair ie and ie between a left and a right context actually corresponds to the definition of a single empty string expressed by
that accepts every substring NUM here an ui is either mapped by the corresponding tq contained in NUM cf
the upward oriented version corresponds to the simultaneous rule application the right and left oriented versions can model rightward or leftward iterating processes such as vowel harmony and assimilation
a regular relation describing replacement in context and a transducer that represents it is defined by the composition of a set of simpler auxiliary relations
null in the relation a x b we call the first lnernber h the upper language and the second member b the lower language
it can be positioned a s a sibling i r examph the following f l ctions are needed fi r a corpus based thesa tlrtls system positiou w ret rns the position or pa th of the word w
i elati u markers resist of cause markers such as g a wo alld n i and adn minal forms of adjecl ives a d a ljefq ive nouns
examples o i the vie tvpoi nts whose typicalness exceeds NUM NUM are as follows i flying vehicle land vehicle water vehicle iic ket balloon a ear train coach s i air plane helicopter cargo ship patrol boat
iuterfaee where eveut s may l e coming from a set of different conqmtationa l device s
su rcb l a predica le is also defined it a t a sk spe cific lira er
lexical functions are used to represent syntactico semantic relations between lexemes such as synonymy hyperonymy and various types of cotlocational relations
lilac query flmction pt1 fly or any other collll iim tion of these capal ilities
edifice rolii coliipoiielll s such a s woodeii illlis a iid ineta l
so although their n grams had been based on general english rather than email their vocabulary was derived from the email data
this report describes the development of a number of techniques for augmenting domain specific language models with data from a more general source
as far as can reasonably be expected the tides constitute a fair and accurate reflection of the contents of each text
to illustrate consider a case where the word of is ranked NUM in one corpus and NUM is another
upon a linear total order over object occurrences in a derivation
this derives relation is denoted and is called linear NUM
for our parsing algorithm we need such a particular derives relation
null we want to parse the input string x a i.e.
its cf backbone is unbounded ambiguous though its language contains the single string a
the production set pd of the ldg d associated with l is
sof course instead of x we can consider any fsa
two productions namely NUM and NUM
l he actual realization of tire interfaces should ground on a sound theoretical framework and it shoukl be as independent as possible from the module implementations
furthermore we show that ice is flexible enough to be used in architectural experiments and we are going to report some of the experiences made with them
each coml onent can i e assigned to a sl ecilie host or wc can leave the assignment of an adequate host to pvm
the testbed is designed as an experimental enviromnent that provides all the features required to test the core components and to study the operation of the whole application
he testbed consists mainly of the following parts the graphical user interface gui provides a comlbrtable dontcnd to the application
the dialogue situation is as follows two business persons speaking difl ereat languages are involved in a face to face dialogue trying to schedule an appointment
remote procedure cmls did not seem to be the right choice either since their use implies a rendez vous synchronization which slows down a system due to network latencies NUM
we will focus on these features in the remainder of this section
this feature encodes whether the agent consciously adopts the intention of performing a
the h rm feature is the grammatical structure of the expression
this paper has detailed a corpus study of preventative expressions in instructional text
we also intend to extend the analysis to ensurative ext ressions
we will conclude with a discussion of the inter coder reliability of our coding
we then filtered the results of this probe in two ways NUM
it attempts to generate and output a first solution producing possible alternatives only on external demand
parameterization of tg NUM is based on specifying the way how the generic backtracking regime should operate
these concepts along with their classification can be used to index any given text for search or categorization purposes to generate suimnaries or to populate database records
we expand our initial set of rules which tallows us to spot more coml anies lteni lcb y kaufman is president of lh nry kaufm an
sw t close to NUM NUM means that t appears imarly exclusively with the candidates that have been accepted by tile spotter and thus provides the strongest positive evidence
the problem of semantic tagging is thus reduced to the problem of partitioning the space of lexical entities into those that are used in the desired sense and those that are not
where f t x is the fl equency of t in group x and s is a constant used to filter the noise of very low frequency items
there are texts e.g. technical manuals where such specialized entities occur more often than elsewhere and it may be adwmtagous to use these texts to derive spotters
this way only the alternative elements of a conflict set have to be expanded from scratch
the main features are a very efficient and robust morphological component a powerful tool for expressing finite state expressions a flexible bidirectional shallow parser as well as a flexible interface to an advanced formalism for typed feature formalisms
the basic design criterion of the system is of providing a set of basic powerful robust and efficient natural language components and generic linguistic knowledge sources which can easily be customized for processing different tasks in a flexible manner
for example the edge mona cat type and n device var will accept a word form which has been analyzed as a noun and whose lexical entry type identifier is subsumed by device
the following example demonstrates the use of these special edges this fst recognizes expressions like sp testens um NUM NUM h by two o clock at the latest with the output description out time rel
for example if an fcp is used for defining generic clause expressions where complements are defined through necessary constraints and adjuncts through optional constraints then it has been shown that the constraints on the adjuncts can change for different applications
because of the lack of comparable existing ie systems defined for handling german texts in similar domains and the lack of evaluation standards for the german language comparable to that of muc we can not claim that these results are comparable
there are two important properties of the system for supporting portability each component outputs the resulting struc null tures uniformly as feature value structures together with its type and the corresponding start and end positions of the spanned input expressions
thus defined a nominal phrase is the concatenation of one optional determiner expressed by the loop operator star n where n starts from NUM and ends by NUM followed by zero or more adjectives followed by a noun
step NUM corpus analysis the following are some of tile result s of l he a nalysis of the
bec use of the in crease in multi rood a expressions the qua lity of tile system improves a s
however it can happen that a combination of more than one ease marker characterizes a sense of the verbal polysenly vj
to this end we basically adopted the text structure of meteer but split her semantic categories into two dimensions following panaget
the upper model is a domain independent property inheritance network of concepts that are hierarchically organized according to how they can be linguistically expressed
then we describe the architecture of our microplanner and illustrate how meteer s text structure can be adopted as our central representation
in this case the following rule combines them into a chain by putting the second derivation into the next slot of the chain
null notice that with respect to simplified english
8f ddfreiner l dcrou du clapet de valve
understatenmnts like 2c are also insincere since they do not provide as inuch illforulation as re quired
in all of the exampies we discuss below the features associated with a conjunction is the generalization of the features associated with each of its conjuncts so our conclusions are equally valid for both the generalization and subsumption accounts of coordination
thus the features on the coordinate structure in 3b must include verb and so do not subsume the verb feature on the np complement correctly predicting the ungrammatieality of 3b
under various circumstances we have linked the software in with either nautilus or the application or have run it as a separate process communicating with nautilus via an ipc socket
during parsing the restriction fires immediately after that nonterminal has been constructed testing the subtree at that point for well formedness or attaching an attribute value for use later on
an experimental study of nl inputs from novice interlace users showed that of NUM inputs NUM contained typos or misspellings and NUM contained ungrammaticalities for an illformedness rate of NUM
our objective in eucalyptus was to make the same command and data access functionality available via natural language integrated as much as possible with the graphical interface to allow multimodal interactions
eucalyptus also includes deictic reference allowing the user to click on one or more radar blips or screen locations while speaking verbal references like this fighter or these cap stations
this had to be modified somewhat in eucalyptus since the koalas world includes hypothetical objects suspected threat aircraft which the user and system can create and destroy at will
the interpreter can either he invoked post parse applied top down to each candidate sentential regularization or interleaved with proteus testing each individual clausal or noun phrase immediately upon construction
the tinsel interpreter is primarily modeldriven which is to say that the case frame behavior of each predicate in the domain must be explicitly encoded in a declarative semantic representation
the main goal of the proposed project is to develop a language model lm that uses syntactic structure
in this case the a cctlra cy
if ttmre are many role lion ships
to overcome the problems of noise 1tha t
bigu tion system there is no need for it NUM o have NUM lie exderlll elll vlel l NUM sevoi li odserv llioiis
overall our results are in much closer agreement with sullivan and damper s word er ror rates of almost NUM on a similar test set
sometimes this failure is a consequence of the lbrm of pronunciation lattice in which nodes are used to represent the end points of mappings
this is an error rate of ahnost NUM as compared to dedina and nusbaum s NUM on the smaller test set of size NUM
differences in test set size and between british and american english the transcription standards of the phoneticians and the lexicons employed seem insufficient to explain this
arguably real words form a much more sensible test set for a pba system than pseudowords not least because they are multisyllabic
in one we replace the second maximum sum heuristic with the maximum product of the arc frequencies we call this model prod
in our opinion a major deficiency of the simple shortest path length heuristic is that the output can become unreasonably sensitive to rare or unique pronunciations
the final product score is not a proper probability for the assembled pronunciation since the scores do not sum to one over all the candidates
again this gave a very significant improvement in run times for the testing of lexical words section NUM NUM below but was unnecessary or the testing of pseudowords
additionally arcs are labeled with a frequency count which is incremented by one each time that substring with that pronunciation is matched during the pass through the lexicon
daille NUM and can be also used as a measure of corpus similarity
as the lm changes it produces different behavior in the combined system and therefore different types of errors e.g.
interestingly this figure is higher than that between email and the whole bnc
it relies on a good classification scheme and reliable organization of the background corpus
the output was a list of the files sorted according to the value of r
it transpires that the mean rank is NUM NUM std dev NUM NUM
it is around NUM better on both measures than the wsj lm
it is therefore possible to rank the rank correlations and hence the bnc domains
likewise the NUM million words classified as commerce and finance may also prove suitable
consequently it was decided to investigate an alternative measure the loglikellhood ratio statistic
moreover the conditions of the rule may depend on the specificity of the representations with which the variables are instantiated
they satisfy x theory which uses well known syntactic concepts independent of any theoretical damework
in NUM we give a graphical representation of a simple and general case of a conversion
as a consequence there will be variables over predicative drss pdrs variables in partial drss
drtheoretic expressions are associated with leaves and then combined to form the final dr s
we are concerned in this impcr with the inl eraction etwe en
ip has the sentence subject np as its specitier and the
figure NUM construction if the rs fl r NUM
we now integrate the semantics and the syntax of sentential negation
the sentence is therefore represented as an inftcxional phrase
table NUM comparison with st ssna NUM
sussna disambiguates several documents from a public corpus using wordnet
his method relies on cooccurrence data gathered on roget s thesaurus semantic categories
the NUM senses related to animals appear in the files animal and food
graphs are drawn against the size of the context NUM
tune the sense distinctions to the level best suited for the application
following this short introduction the conceptual dcnsity formula is presented
results are given averaging the results of the four files
this baseline was first calculated analytically and later checked experimentally
both file matches and sense matches are interesting to count
the email lm outperformed the other l ms on real spoken data albeit taken from a technical ernaimike domain for eight of the ten speakers
to illustrate consider the distribution of unigram frequencies a mere NUM NUM word types NUM in the email corpus have frequencies of NUM or greater
of the top ten texts six have titles that are clearly related to computing including all of the top five
typical examples of horizontal redundancy in the hierarchical lexicon thus conceived arc the alternation phenomena e.g.
extra machinery for blocking these rules in order to account for exceptional behavior is also necessary
these are additional to the typed multiple inheritance network which already structures the lexicon
tlowever it must be noted here that this is not always a simple task
on con obl with con opt with con bl figure NUM type system fragment encoding prepos itional alternation
the lexica entry for to load would look as in
consequently no ambiguity problems result with a nice effect on parsing time
these strategies an be achieved with bottom up processing
bug creates the passive arc NUM
we have compared translation times in the tdmt prototype system for two cases
we have experimented with the translation times of some english sentences into japanese
this information is used when NUM is combined with another substring
x is the target expression corresponding to
noun phrase np np cn proper noun
table NUM shows examples of the relationships between linguistic levels
shown to be efficient and particularly promising for spokendangnage translation
our algorithm for bottom up application of patterns is as follows
the results indicalc that mi l conw rges to the true inode fasl er
man who gave his paycheck to his wife was wiser than the one who gave it to his mistress
the clynamic account raises the following problem since the index of the tile initial controller is reassioned it becomes inaccessible in subse null served tor the discourse center and the discourse center will always occupy another index as well as NUM we will us the to designate references to the discourse ce nter
in this paper we have proposed a unified theory of irony that overcomes several difficulties of previous irony theories
another reason is that studies of irony have been regarded as of no practical use for nlp systems
the propositionp s c is true if s supports v and otherwise false
section NUM presents our unified theory of irony that can cope with the problems and its computational formalization
for example information that candy eats the pizza is represented as the infon eat x a in which x and a denote candy and the pizza and its negation as ca x a NUM
when we omit an actioi a froin a ausal relation that relation ccomes a constraint in sitltation theory denoted by sl cq s2 NUM figure NUM illustrates the represe ntation of ironic environnlents of exalnple s NUM and NUM
we have posed the problem of coherence as regarding the knowledge represented in the knowledge base taking into account the apparent contradictions within discourse
the incoherence can be result fi om a lot of phenomena but we restrict ourselves in this communication to incoherence stemming from negation
in our current work we are investigating how more of the design knowledge call be made accessible md uimel standable to the technical authors and what other tools would further facilitate tile authors task
the drafling tool takes this reprcscni at ioil as input and produces english an l f y ench draf ts of t he appropriaw tul orial inslxu tions
tirs of hese asks is lmrfornmd using a ontrolle t nalalra NUM bm guage inl erfa wlfile the s md is done wit h a lialog box lllc haltisill
when the author initiates the drafl ing tool see figure NUM m af rl t calls the text planner with the discourse goal make the user colnpetent to perform tile action specified by the author
once the action nodes of the graph have been created or perhaps while they are being created the author has the ability to link them together using a set of predefined procedural relations goal precondition sub action side effect warning and cancellation
i raftei s general architecture shown in figure NUM is based on two inain processing modules tile author interface shown oil the fitr left of the diagram allows authors to build a task model and to control the drafting process
as noted in section NUM datr nodes can be thought of semantically as denoting partial functions from paths sequences of atoms to values sequences of atoms
a transitive verb tr verb is both a verb and a word that takes an np complement thus it should inherit from both verb and np arg in this analysis
but a grammar or a parser that expects to see dags represented as they are here can interpret the datr values as easily as it can the contents of a file
a direct encoding for this is as follows in these revised definitions the right hand side of the syn cat statement is not a direct value specification but instead an inheritance descriptor
thus the following arise NUM tense plur did tense sing three did participle plur done participle sing one done
for example when a global path is specified it effectively returns control to the current global node often the original query node but with the newly given path
for example if sentence lie is generated first considering that more specific verbs should be privileged a rephrasing request would cause the generator to propose an alternative realisation based on the general verb remove which allows to express at surface level the argument left implicit in the first proposal
in this particular case a clarification question is generated
constraint NUM subject of sentence with ability expressions a subject of a sentence with the expressions of ability or permission must have his her intention to make a choice about the action described by the sentence
since tile manufactm e is the speaker and the user is the hearer according to the constraint of the discourse situation the mauufacture ltll l tile user can not be the subject of the matrix clause
we expect that by pragmatic constraints the ambiguity in manual sentences would be resolw d to some extent not in the process of inference but in the process of the translation of manual sentences into semantic representations
to examine the accuracy of interpretations bused on our estinmtion we have collected about NUM sentences which include to and some of which also inch me possibility expressions from several types of inanuals l y these sentence s
since recently there are many machines whose operating procedures are complicated we have much trouble in many cases including translating their manuals into other languages maintaining consistency between the description in manuals and the actual behavior of the machines
moreover fi om the fact that the matrix clause of to and reba can not express the speaker s attitude we pragmatically infer that tara and nara are expected to be used only for expressing the speaker s attitude
since the constraints are efl ective in the lifferent target from ours the accuracy of identifying the referents of zero pronouns would be improved much more by using both of his constraints and the constraint we proposed
generally speaking almost all systems described above take the following scheme l irstly each sentence in a text is translated into a semantic representation hi this process the system uses only non defeasible syntactic and semantic collstraints
we expect that there are few cases that the subject of the matrix clause is a machine because the highly context specific assumption which is expressed by tara or nara is not suitable for tile description of general rules
such formalisms typically include a context free cf base which allows the use of parsing algorithms designed for cf languages despite the fact that complex feature based formalisms are essentially more powerful than cf grammars
this has far reaching consequences for the analysis of inflectional morphology and lexical items for which no entry at all or no adequate entry is found in the parser s lexicon
at the lnoment this schema is used for a few dedicated data structures e.g. for speech data or arbitrary prolog terms which may be even cyclic
r he contrast recalls an ohl debate over spoken language as to whether its properties are driven by hearers acoustic needs coml rehension or speakers articulatory needs generation
the only point at which this matters is the time of the first message sending attempt which will be blocked until the target component registers at the ils
when porting the system to another domain parts of the integration process have to be repeated
an interesting fact about recent word sense disambiguation algorithms is that they have made use of different orthogonal sources of information the in
the three types of transitions operate as follows
the dot thus refers to the current position i
states are derived from productions in the grammar
for some u generate the suffix xi
computational linguistics volume NUM number NUM rationale
stands for an arbitrary continuation of the rhs
completion without truncation would enter an infinite loop
subjects were chosen such that e had enough knowledge to solve the problem but n did not
we presented a computational model of producing utterances incrementally so as not to make excessively long pauses
awe have omitted other method to avoid intinite reeursive application of the method r7
constraint e3 was used in NUM to topicalize the musashino center
for example consider the domain action of moving from one location NUM to another NUM
the information units iks for short are regarded as minilnal components of discourse structure
the contents of these actions are written as r2
part of the content of this plan is represented as follows
this model can utilize such a discourse structure to incrementally produce utterances according to pragmatic constraints
the problem solver makes domain plans that solve a given problem
for demo purposes we opted to implement a NUM word speech interface containing just fifty german proper names few enough that the recognizer does n t have too much trouble distinguishing them for an input coverage of about NUM million utterances
null since the pe200 s phonetic rules are for american english and unlike proteus the module can not be tricked into recognizing unknown inputs as possible proper names a complete speech input component for interlace was impractical
in interleaved mode if a node s regularization does not pass the case frame or selection criteria then the node is not added to the chart which can prune the search space and reduce parsing time considerably
another goal in interrob is to go beyond the usual restriction of deictic reference to demonstrative or indexical references that here there and allow gestures to accompany any sort of definite or indefinite np
this means that phrases like that waypoint or the waypoint over there must be assigned a special focal extension a pseudo object called a gesture waypoint which is not one of the four actual waypoint objects in the closed world whereas with query capability nautilus might be able to obtain enough information from the robot to determine which of the four actual waypoints is being gestured toward
we did not have time or resources to tackle the problem of resolving referents based on visual context for example having that helicopter refer to the one nearest the center of the user s field of view but we are currently investigating the interaction of vision and language in the interrob project to be discussed shortly
for example a nl command to the system might result in the display of the same dialog box used in the corresponding gui command but with the dialog s data fields fully or partially filled from the nl input the user can then fill in any remaining empty fields and issue final acceptance either graphically or verbally
to avoid having to enter hundreds of foreign proper names into the proteus lexicon we modified the proteus lexical tagger to assume that any input word might be a proper name applying that assumption only to non english words failed the first time we encountered the river main and the czech towns of most and as
we describe our experiences building spoken language interfaces to four demonstration applications all involving NUM or NUM d spatial displays or gestural interactions an air combat command and control simulation an immersive vr tactical scenario viewer a map based air strike simulation tool with cartographic database and a speech gesture controller for mobile robots
as originally designed focal expected a closed world model of all domain objects to be available at startup time
the following subsections describe the design objecfives and goals of spot
multi lingual support however is very difficult to obtain commercially
a system of multiple fdfs can linearly expand the search rate
it needs to provide hundreds of users with access to this database
development is currently proceeding to interface spot to an excalibur conquest archival database
this text search tool is called spot
except the subject tom or apple
c6mmoil sei g mm ilffc 4rence
in sentence NUM of tile figure the mo lifiee of the prel ositional phrase with a telescope can be either saw or girl depending on its context
this method can NUM e used to solve context dependent t rol leuls such as the wellknown examt le shown in figure NUM
for twb however the performance difl erence between test sets is less consistent
this is not a mere union operation but a union operation accompanied by frequency counting
the experiment shows that the proposed method is effective in reducing the cost of bilingual dictionary augmentation
in sec NUM we describe the hasic idea of our methud and give an overview
natural language texts are composed of two types of words content words and function words
the quantitative profile of the sample patent documents is shown in table l a
we also ascertained that repeating the feedback one more time did not result in significant improvement
a good way to evaluate word correspondence extraction methods is to measure their recall and precision
in an experiment with patent corpora NUM NUM pseudo recall and NUM NUM precision were achieved
NUM refinement of nominal compound extraction procedure the simplified procedure described in sec
for example in victoria and albert museum the conjunction is within the scope of the lexical head museum because museum is a noun that can take pp modification museum of natural history and hence pre modification natural history museum
for example if a new name such as ibm credit corp occurs in the text but not in the database while ibm exists in the database automatic identification of ibm should be blocked in favor of the new name ibm credi corp
from nominator s point of view all three operator types behave in similar ways and often interact when they co occur in the same name sequence as in new york s moma and the victoria and albert museum in london
for example if the name lacks a personal title and a first name and its last name is listed as an organization word e.g. department in the authority list it receives a high negative score
section NUM discusses the role of context and world knowledge in their disambiguation section NUM describes the process of name discovery as implemented in nominator a module for proper name recognition developed at the ibm t j watson research center
identification of the type of proper nouns resembles the problem of sense disambiguation for common nouns where for instance state taken out of context may refer either to a government body or the condition of a person or entity
if the heuristic for a certain entity type a person for example results in a high condifence score highly confident that this is a person name we determine that the name unambiguously refers to this type
a database also has the potential to resolve structural ambiguity for example if ibm and apple computers are listed individually in the database but ibm and apple computers is not it may indicate a conjunction of two distinct names
the components of victoria and albert museum and ibm and bell laboratories look identical however and is part of the name of the museum in the first example but a conjunction joining two computer company names in the second
the interaction predicate encoding the finite state automaton of figure NUM is shown in figure NUM
the principles encoding the extended lexicon in such an approach are shown in figure NUM
during word class specialization that keeps track of the feature structures obtained for each node
a further improvement relevant to on the fly application of lexical rules is presented in section NUM
the definite clauses thereby introduce what we refer to as systematic covariation in lexical entries
the set of follow relationships is obtained by testing which in specifications unify with which out specifications
in the latter case we can also take care of transferring the value of z
meurers and minnen covariation approach to hpsg lexical rules derived entry one is looking for
in lexically oriented grammar formalisms like hpsg the lexical entries are highly information rich
definition NUM the following definitions are relative to a given scfg g
clarify b a complex value NUM a complex value is a feature structure
in thi pn er we propose an alt ernative method based oil word lewd sorting
the second step of the method evaluates the statistical similarity of the word chunks appearing in the corresponding sentences
these collocations are more useful for translators than noun phrase collocations but greatly differ from domain to domain
lia ample naphtha and gas oil rose on the oil products spot market in singapore
in this stage we count how many times a string whose adopt is NUM appears in the corpus
finally to determine which word chunks to extract the pointer table is sorted once again in alphabetic order
next in order to remove useless subsumed strings the pointer table is sorted according to sentno
we count these NUM resolutions as false positives since the anaphor has been resolved to the false discourse entity
NUM compute the c un considering only the elements of the matrix clause of un
these errors occur whenever an inter sentential anaphor can be resolved with an incorrect intra sentential antecedent
c propose all dements of un not yet checked from left to right
grosz et al suggest the processing of sentences linearly one clause at a time
only NUM errors of the functional approach can be avoided by incorporating semantic criteria
NUM ist der resume modus aktiviert schaltet sich der t3100sx selbstiindig ab
we have chosen in this article to present our treebank in some detail rather than to compare and contrast it with other treebanks
the question arises as to which strategy fits best for the interaction between the resolution of intra and inter sentential anaphora
in addition we maintain that exchanging grammatical with functional criteria is also a reasonable strategy for fixed word order languages
ar l ars xl wil li resl iecl t o the a7 i7 lgnglish tran mar
optimality theory figure i search spaces within different paradigms
2for brevity we are not considering other candidates
the search narrows in on an optimal output figure lb using evaluation constraints in a process called eval successively smaller boundaries are cut out by the constraints until only one candidate remains
we can encode a similar fst for nocoda
in the ot derivation of grumsdwet from um gradwet figure NUM the winning candidate violates nocoda twice while the first two candidates violate it three times
this produced the following top and bottom NUM texts
we set out to establish whether the task model mone is sufficient to control the linguistic output of a text generation system or whether additional control is required
a key declaration which the grammar developer may do identifies the atomic value which is to serve as a key
we define a link token to be an ordered pair of word tokens one from each half of the bitext
n b k and need not sum to NUM because they are conditioned on different events
the upper curve represents precision when incomplete links are considered correct and the lower when they are considered incorrect
though some have tried it is not clear how to extract such accurate lexicons from other published translation models
the hidden parameters can be conditioned on information extrinsic to the model providing an easy way to integrate pre existing knowledge
in contrast the dynamic nature of the competitive linking algorithm changes the pr datalmodel in a non monotonic fashion
a bitext comprises a pair of texts in two languages where each text is a translation of the other
the factors on the right hand side of equation NUM can be written explicitly with the help of a mixture coefficient
equating the right hand sides of equations NUM and NUM and rearranging the terms we get
we define the recall of a word to word translation model as the fraction of the bitext vocabulary represented in the model
alep is designed to support efficiency as far as the formalism lean approach is concerned
in the process of spanish acquisition NUM of all entries were created from scratch by h level lexicographers and NUM were generated by lrs and checked by research associates
run time application of lrs at run time raises additional difficulties by not supporting an index of all the head forms to be used by the syntactic and semantic processes
around NUM english adjectives out of the NUM NUM or so which occur in the intersection of ldoce and the NUM NUM wall street journal corpora end in able
adjectives like audible or legible do conform to the formula above but they are derived as it were from suppletive verbs hear and read respectively
from the NUM different citation forms shown in figure NUM only NUM forms see figure NUM featuring NUM new entries have been accepted after checking
however the citation forms supercompra precompra precomprado autocomprar actually appeared in other corpora so that entries for them could be generated automatically at run time
depending on the paradigm or approach there are phenomena which may be more or less appropriate for treatment by lrs than by syntactic transformations lexical enumeration or other mechanisms
for example the verb destroy may be represented by an event as will the noun destruction naturally with a different linking in the syntax semantics interface
for example knowing bank in my bank is on the corner is being used as a noun will tell us that the word is not being used in the plane turning corner sense but not whether it is being used in the financial institution or edge of river senses
this is t hercfore a step towards aut omatically building the knowledge base required for the generation system
lastly building thesauri by hand requires significant amounts of time and effort even for restricted domains
each value in the table is the number of correct cases with its percentage in the parentheses
a kanzi character is an ideogram and has a distinct stand alone meaning to a certain extent
nakano first constructed a kanzi meaning dictionary from bgh by extracting words including a single kanzi character
this is estimated from the relative frequency of v co occurring with noun w namely
word belonging to a given word class with the probabilities calculated using nounverb co occurrence pairs
it is sometimes impractical to build a large thesaurus from scratch based on only co occurrence data
rows show the distribution word numbers on the basis of occurrence frequencies in the training data
the performance of k nn is noticeably worse than that of the others for low frequent words
we adopted a probabilistic model which has a sounder foundation than the uramoto s
logical form the government and binding level of semantic representation
in tile case of out admittedly simple example seven of the nine actions in the procedural structure are automatically specified
there is now a large body of past work on wsd
the suggested concept types arc cc surgical deed without subtype ccanatomy cc mthology cc intervent equipment and cc way
for the guessing of the non surgical deed concepts it uses the constraints given tbr the fillers of the slots of the surgical deed frame
this however is not represented in order to keep the figure clear
figure NUM accuracy of dependency and adjacency model for various training schemes
sit either probability estimate is zero the other analysis is chosen
for comparison the untuned accuracy figures are shown with dotted lines
for all larger windows neither model is ever forced to guess
in all cases the estimates used are
when it is less than unity a right branching analysis is chosen
in and is in the public domain
the intuition here is that a geometrical mechanism centers on the highlighted item
our most recent voice dialogue system incorporates many of the ideas outlined above
figure NUM a multimodal dialogue system using mes sages and events
ditaftei lcb is also able to infer the basic interface actions that can be performed on the various interface widgets and creates task model instances for them as well
i have argued that without it a broad coverage system would be impossible
this result is in accordance with the informal reasoning given in section NUM NUM
making it the prefered output mode
this is explained in the next section
in other words s and s pose different scopes on structure sharing tags in addition we also extract a feature structure f reached by a path or an attribute NUM in a feature structure ip
the top line of text reader save information shows the current state of the cnl specification
execution time of our i arsing method and a more naive algorithm which l erforms phase NUM parsing with las and al plys rule s hemata to olnph ted
the sub fs r contains the synlcounter and the value is treated at phase NUM the other problem i.e. termination of dcps often occurs because of underspecification of the nork head dtr wines
lo use abridged expressions whi h causes integration of multi modal interpret at ion and cont ext
in tim level NUM section whethe r di l erent mode hq uts express identica l
ased cm the a ualysis of sele le l corpus e xpressio ls
l he design rocess of t he multi moda l method lies seve l stel s
in this paper we lea with cite synergist it category the most
especia lly in willdow based systelils sollie tyi e s of inl er a ce
thus in contrast to employing mdl it will not have the effect of smoothing a t all
each pattern can be matched against nodes in a tre e alence class of noun phrases
sentences wit h NUM NUM words can usually be parsed within NUM seconds on a pentium NUM pc
for example the word japan is also listed as a transitive verb s
the scenario templates are generated in two steps first a post holder database is created
NUM one of the many differences between robert l
NUM there are no immediate plans to replace mr
the co module is trained on the NUM dry run test articles
we estimate that NUM person days were spent on the following components tasks
example NUM the list NUM is what we used in our muc NUM system
st the st recall and precision for the article are NUM and NUM respectively
so if a semantic dictionary contained only two senses for stake that vague sense together with stake as a post then one would expect to assign the vague sense for both the sentences above
the former apl roach is called divisive the latter agglomerative
as such a kind of knowledge we pay our attention to pragmatic constraints which haw not been used sufficiently in the former methods
note that a subject should be either a user or a machine because manufacturers have finished all the actions appeared in the context of instruction before shipment
NUM c kono botan o osu to a n m this button ace push to c b der are mas u
thus in a majority of cases ps negation seems to be used to assert the absence of an event and it is very difficult in those cases to find a real event which could be seen as denoted by the sentence
frequency of the noun verb pairs belonging to cluster c
to verb and if the latter is larger tha n the former we attach it
in particular the string probability p x x is the sum of the probabilities of all paths starting with x that are complete and constrained by x
we artificially constructed a true model of word co occurrence and then generated data according to its distributiou
the design of semantic pragmatic features usually requires a series of iterations and modifications
because of its syntactic nature the form feature coding was very robust
the dislinclion between i ri lcq h andaxlwct table l is theoretically intportant because an aspect represents tile l roperty o1 dialogue addressed by a particular maxim or prhlciple
in woz iteration NUM for instance a subject expressed surprise at not having been offered the option of being put on a waiting list in a case in which a flight was already fully booked
the principle explicitly introduces two notions the notion o1 interlocutors background knowledge and that of possible diltcrcnccs in background knowledge between diltercnt user populations and individual users
grice s nmxims of truth and evidence gp3 7i NUM have no coui terparts aniong ttr t inciples but inay simply be inchided among the principles
however designers of such systems are continuously confronted with questions about what the system should know and what is just within or barely outside the system s intended or expected donmin of expertise
based on this recognition a speaker either ah cady has built prior to the dialogue or adaptively buikts during dialogue a model o1 the interlocutor which serves to guide speaker coopcrativily
the aim of a parser is to take a tagged sentence as input for example figure l a and produce a phrase structure tree as output figure l b
probability estimates are based on counts of consecutive pairs of words in unreduced training data sentences where basenp boundaries define whether gaps fall into the s c e b or n categories
non head words within basenps are excluded from the dependency structure d b the set of basenps and d the set of dependencies are extracted from c
the model we have described thus far takes the single best sequence of tags from the tagger and it is clear that there is potential for better integration of the tagger and parser
table NUM the contribution of various components of
table NUM the trade off between speed and accuracy
second a beam search strategy is used
the process continues until a parse is found
ifb is a goal and b is a parsed left corner such that x x e l and b u x and b u x exist then there is a link between b and b we can stop here with a mere test of unification if we only want to use linking as a filter to reduce the search space
if any nonterminal x admitted infinite derivations with nonzero probability then s itself would have such derivations since by assumption x is reachable from s with nonzero probability
it has been presented so often since and is now so welbknown that a brief informal statement of the algorithm should sufrice here the algorithm applies to cf grammars in general it is both correct and with the exception of derivations of the form a a where a is a nonterminal is complete
this involves cases where no entry at all is found for a given word form but also cases where an entry for the form is found which however does not fit the given context the missing lexical entries may simply have been omitted from the lexicon of a system or may reflect novel lexical creations
but rather than computing the generalization of left recursive categories to avoid the possibility of generating an infinite relation they instead impose a cutoff after two nested occurrences of the same functor in a feature specification substituting new unique variables for the arguments of the inner occurrence of the functor so that any constituent with a more complex feature description will be accepted
s np vp s head vp head s head form finite vp subcat first np vp subcat rest end
needless to say the effectiveness of this method is highly dependent on the s mrce text and it may seem too optimistic to expe t such useful information ill the same context
in spite of the sin t lmty of our context model some elliptical phrases can be supt lelnented by using information extracted h om the context model
word sense a result of word sense lisambiguation ai plied in one sentence cau be shared with all tiler words in tile context that have the same lemma
r sltlt NUM f syn tacti miaiysis ofeach sexitencd in t li i ext as ontext ilfformation thus
figure NUM shows the translation outputs of our syste n with and without information NUM rovi h d by context pr t essing
fo using sul jnncts lr tw m tention to a part of t senten e th tt often represents new information
ble NUM NUM worst possible NUM NUM
statistical microfeatures are represented for each word as a vector of continuous values vstat
these microfeatures each of them representing a feature pair are extracted automatically
in this paper we present a parser that produces complex feature structures as known from e.g.
figure NUM feature structure parse figure NUM chunked and labeled sentence labels
most networks have a certain error rate only a few networks are perfect
the evaluation environment is the janus speech translation system for the spontaneous scheduling task
the chunk n label principle is the basis for the design and implementation of the feaspar parser
iligit fre tuency closed class words like a the on etc are excluded via a stop list file
a flirther difference is the non standard method of tf idfweight ah ulation timy are using for their system
in both cases the coverage loss due to grammar specialization was about NUM to NUM using training corpora with about NUM NUM examples
as a result full parsing is very quick and only one analysis the correct one is produced for the sentence
experiment s with methods that normalized for sentence length yiehled worse results so dtis bias appears to be api roi riate
however in the context of a specific domain most of these will be extremely implausible and can in practice be ignored
as in the original models the language model heavily influences the remaining ordering decisions
conventional architectures for chinese nlp generally attempt to identify word boundaries as a preprocessing stage
first we are not merely parsing but translating with a bigram language model
for comparison the accuracies from the a based systems are also shown
these arrangements will enhance our ability to maintain monetary stability in the years to come
the pragmatic benefit is that structured grammars become easier to write and more concise
on the other hand the new algorithm has indeed proven to be much faster
the new translation model is based on the recently introduced bilingual language modeling approach
finally the argmax operator is generalized to vector notation to accomodate multiple indices
in the attribute value matrix notation that we use to display underspecified feature structures the type marked with an asterisk is the most specific lower bound of the types in its scope
there are immediate effects in improving precision
the noun phrase the museum does not refer uniquely to one object as shown in figure NUM thus an underspecified feature structure is generated on the object level
in the remainder of the paper the level of representation may also be referred to by a number ranging from NUM object level to three orthographic level
to specify the interface with the dialogue system each agent exports a set of signatures containing information about the number and form of the procedures parameters to the general manager
is possible the path value of an underspecified feature structure being the underspecified structure of all values of the path when applied to the feature structures represented by the underspecified feature structure
evaluation of a predicate or function means to pass the variable values to the procedure implementing the predicate or function and to leave control to the agent associated with the procedure
for this reason the system paraphrased the noun phrase conveyed by the user to refer to the objects and the minimum and maximum prices are filled in a template
a linguist manually identified NUM occurrences of proper names which reduced to NUM unique tokens
this is the case with justice department or frank sinatra building
we have observed that this type of heuristic works quite well
if the scope strength is similar the string is split
in addition the authority file contains about NUM NUM first names
nominator s performance deteriorates if other conventions are not consistently followed
next the second and from the right is evaluated
our choice of disambiguation resources makes nominator fast and robust
l irst the hou analysis makes minimal assumptions about the role syntax is called to play in determining the i sv
an imi rcb ortmlt consideration he re is to mainlain all overall precision level throughout the elltire process
both prediction and completion steps make use of a matrix r defined as a geometric series derived from a matrix p r i p p2 i p NUM
though not truly probabilistic these algorithms are similar to the viterbi version described here in that they find a parse that optimizes the accumulated matching scores without regard to rule probabilities
first 0t s is completed yielding a complete state 0t s which allows 0s t to be completed leading to another complete state for s etc
for a grammar with NUM nonterminals of which only NUM have nonterminal productions the left corner matrix was computed in NUM seconds including the final multiply with p and addition of
in this paper we have introduced a section of an intelligent computer assisted language learning system that attempts to capture the user s current generation capabilities
name recognition is completely integrated in template entity extraction so the system is ready for further incremental augmentation toward a fullscale ie system
we describe its system architecture strengths weaknesses and its contribution to the prospects of a full information extraction system
organization person and location names comprise a majority of the names to be recognized and a special difficulty arises when they occur in similar linguistic contexts
the system must hit the right balance between the size of the dictionary of known names and the complexity of the name context patterns
at the end of each sentence loop the merger merges the new and existing template objects produced from the document so far
the other method taken by the sri system in met is to remove these complex morphemes from the dictionary and combine sublexical items with rules
the ascii tokenizer is identical to the english fastus tokenizer which recognizes alphabetic alphanumeric numeric and separator tokens as well as sgml tag tokens
as a result the parser considers ordinarily any auxiliary an ordinary mod argument of the verb
the latter is roughly modeled by two semantic fields the task domain and the computing domain
at first the contextual adaptation favors the priming words which are consistent with the semantic context
or consider the polysemy of cherry NUM NUM cf
en verb mor past participle mor root en
evans and gazdar lexical knowledge representation NUM NUM abbreviatory variables
first we will show how a corpus investigation estabfished the basis for the coverage second how various phenomena deternlined by corpus investigation are treated in text handhng th third how the linguistic modules two level morphology tlm word and phrase structure the lexicons look hke
an alep tl rule comes as a four to five place prolog term the first argument being the rule name the second a tl description the third represented by the anonymous variable a specifier feature structure the fourth a typed feature structure constraining the application of the rule and a fifth allowing for linking variables to predeflned character sets
the major reason for such a treatment lies in the fact that it allows for a unified treatment of all functional elements like inflectional affixes complenrentizers auxiliaries infinitival zu functional prepositions etc
this paper describes results achieved in a project which addresses the issue of how the gap between unification based grammars as a scientific concept and real world applications can be narrowed down NUM application oriented grammar development has to take into account the following parameters efficiency the project chose a so called lean formal ism a term encodable language providing efficient term unification alep
this however is not an ordinary unfolding tree as it is constructed on the basis of an abstract seed i.e. a seed adorned with a specification of which arguments are to be considered bound
the effect of taking data flow into account can be observed by comparing the rules for mag c vp and mag c np in the previous section with rule NUM and NUM in figure NUM respectively
step NUM of the algorithm results in the following modified version of the original grammar rule s p0 p vform ssem magic s p0 p vform ssem vp pl p vform csem ssem np p0 pi csem
can be eliminated by unfolding the magic s literal in the modified s rule s po p vfop ssem magic s vform ssem null vp p1 p vf01 csem ssem np p0 p1 csem
if the s rule in the running example is not optimized the resulting processing behavior would not have fallen out so nicely in this case it leads either to an intermediate filtering step for the non chaining sentence rule or to the addition of the literal corresponding to the subject np to all chain and non chain rules along the path to the semantic head
in the fall of NUM NUM duke undergraduates used the duke programming tutor in place of their regular weekly lab
in our chart parsing an action is represented by an edge
el NUM d2 e2 NUM d1 e3 s4 d2 e2 s1d1 leis message plan generator c c ontologizer fuf contentplanner lisp lexica izer fuf
in fig NUM equipment has rank NUM because it has NUM distinct equipment values all dlc dlc and dss dlc date has rank NUM because it has two distinct date values NUM q1 and NUM q3 site has rank NUM attribute class and action fig
this research was conducted while supported by bellcore project cu01403301a1 and under the auspices of the columbia university cat in high performance computing and communications in healthcare a new york state center for advanced technology supported by the new york state science and technology foundation
merging two messages with two or more distinct attributes will result in a syntactically valid sentence but with an undesirable meaning this refinement activated all dlc and dss dlc for csas NUM and NUM in the third quarter of NUM
step NUM is applied to the message list recursively to generate possible crossing conjunction as in the following output which merges four messages this refinement activated all dlc and dss dlc for csas NUM and NUM in the third quarter of NUM
comparing the third message e3 NUM d2 to el NUM d2 they have different equipment and site but not date so a sentence break will take place between them
note that although el NUM d2 and e3 NUM d2 have the date in common they are not combined because they have more than one distinct attribute site and equipment
input to the message generator comes from leis plan tracking files which record user s actions during a planning session
a bottom up NUM step algorithm was developed NUM sorting putting similar messages right next to each other
there are three character classes used in writing japanese kanzi hiragana and katakana
bgh includes NUM NUM words each of which is assigned an NUM digit class code
a more straightforward calculation would be one based on the relative frequency of words belonging to class c
he then assigned class codes to new words based on this kanzi meaning dictionary
many people have suggested that free word order languages order information from old to new information
the cf list is usually ranked according to a hierarchy of granmmtica relal ions e.g.
then a discourse new topic can be placed in the sentence initial position to start a new discourse segment
note that the inappropriate word orders indicated by can not be generated by the algorithm
as for this notebook i like it very much bunu da baban ml verdi
the fo cus is the new or important information in the sentence and receives prosodic prominence in speech
if there is no discourse new information the second step in the algorithm allows contrastive focusing of discourse old information
NUM lnferrables refer to entities that the hearer can easily accmnmodate based on entities already in the dis
what we can ask about a node in the source treeb b parse is either what its non terminal label is or how many children it has
figure NUM shows an extract from one such interview
treeb ulc conversion models are trained on NUM NUM NUM mning words of atr lancaster treeb uk together with aligned ibm lancaster treeb
the atr lancaster treeb nk ng effort features a grammarian who originated the grammar and a treebanking team who apply the grammar to treebank text
since montague a major goal of semantics has been to describe a compositional method for converting a syntactic representation of a sentence into a logical representation of the sentence meaning and dmn to evaluate that representation with respect to a given context
obviously no direct comparisons of the results of tables NUM NUM with previous parsing work is possible as we are the first to parse using the treebank
an impression of the di cnlty of the treeb nk conversion task undertaken here can be gained by closely contrasting the two parses of this figure
we have chosen to explore the problem using an even simpler approach ignoring the atr treebank and working only within the model for p aif
all of these parameters are associated with kinds of flexibility which seem to be desirable in the exploitation of the dynamic hypertext concept but they are not necessarily independent or all useful together
during further training the supervisor then enters parse action commands by either confirming what the system proposes or overruling it by providing the proper action
eat argl i arg2 ice cream with spoon NUM and eat argl i arg2 ice cream with spoon
for each training sentence the system and the supervisor parse the sentence step by step with the supervisor entering the next parse action e.g.
kr c anned full canned initially yes user initially no kr i ntermed
this approach allows the hypertext pages seen by the user to be customised in relation to the browsing context
because hard in its not easy sense also modifies concrete nouns syntactically on the surface though not semantically see section NUM concrete does not as reliably indicate the not soft sense of hard
since the two instances of wine in the NUM sentence samples are of this sort their old modifiers are properly assigned to not young we assigned them to not new under a literal interpretation of animate
accordingly although most instances of nouns for text types can disambiguate short by being concrete the principled basis for disambiguating the adjective entails a more complex type of inference than simple characterization of semantic attributes of the modified noun itself
some of these such as shell and tank have meanings to which the load bearing issue is not relevant though in the case of tank the application of the adjective light does appear to restrict its referent to the military vehicle
in that case ww is relatively even easier t o process since it costs wl NUM to parse with the metagrammatical approach but ww i will cost NUM wl NUM in tile direct approach
the information in the noun phrase must be specific enough to reduce ambiguity in the underspecified structure
the condition of the first rule yields true if the description refers to more than three objects
after unification another procedure ensures that the new information is inserted correctly in the discourse blackboard
the variables in the rules may be instantiated with representations on each of the four levels
if a rule contains variables variable substitutions have to be calculated before evaluating the rule
the agents implement operations on the representations stored in the discourse blackboard in a modular way
these techniques achieve most of the advantages of lexicon expansion in the face of recursive rules and cyclic rule interactions which preclude a full off line expansion
for comparison we show whether the word is found in the cambridge international dictionary of english cide a modern learner s dictionary
choosing between these rules in the absence of clear contextual information could be achieved by choosing the derivation and thus interpretation with highest probability
where n is the number of attested lexical entries which match the lexical rule input and m is the number of attested output entries
consider the lexical entry for the verb fax given in figure NUM and assume the verb is unattested in a dative construction such as fax me the minutes of the last meeting
creation or transfer verbs provide necessary conditions for lexical rule application but that narrow class lexical rules should be specified breaking down such rules into a number of fully productive subcases
we have concentrated on sense extension but the same machinery could be used for derivational morphology with the advantage that acquiring frequencies from corpora is easier at least for unambiguous affixes
both these problems require an account of the interface with pragmatics though the latter is perhaps not serious for computational applications since we are unlikely to want to generate blocked forms
NUM firing execute its side effect code if any
figure NUM shows a sample sentence from the ei r corpus and table NUM shows the computation of the one character environment of noun in the tiny corpus consisting of this single sentence
notice that some extracted words consist of more than one type of character such as NUM protein
n is tile number of poss in consideration
we therefore reason that the question is to find the set of p posk let which minimizes the difference between both sides of formula NUM in terms of some measure
NUM fill the show slot with arrival time
for each combination of ft and t the beam search procedure considers all possible combinations of fill operations while pruning partial theories that fall beneath the threshold imposed by the beam limit
NUM semantic interpretation semantic syntactic parse trees are immediately useful to the semantic interpretation process semantic labels identify the basic units of meaning while syntactic structures help identify relationships between those units
system2 displays boston to denver flights for in user2 it is obvious from context that the user is asking about flights whose origin is boston and whose destination is denver and not all flights between any two cities
since p w is constant for any given word string candidate parses can be ranked by considering only the product p t p w i NUM
these direct copying operations are assigned probability NUM
as briefly noted above we are using a channel abstraction to model communication between components
figure NUM split channel contiguration two components a and b are connected us
modules can be attached to listen to data transported on a channel or to inject messages
the architecture of a system using ice as communication framework is depicted in pig NUM
therefore it interprets the dialogue on demand in certain situations
null the second phase handles the notification of the target component
the decision which colnl onent will rtm on which host of the network is conligurable
the parser then tries to integrate the new hypotheses into existing partial analyses constructed so far
this is done by sending a message containing the name of the component to the ils
the channel endpoints are split up to allow visualization of message data sent by either component
NUM only the midday sun i NUM rc nt at izopical NUM farther from the latitudes b warm evidence
besides directly comparing the trees built by the program with those built by analysts we also evaluated the impact that our trees could have on the task of summarizing text
since a shallow analyzer can not identify with sufficient precision whether an occurrence of and has a discourse or a sentential usage most of its occurrences are therefore ignored
for example when an although marker is found a flag is set to instruct the analyzer to break the current sentence at the first occurrence of a comma
NUM previotlslyfrozen NUM aegr s c n tl NUM carbon oxi tpoles
the algorithm is then recursively applied on the text that is found between the occurrence of although and the end of the sentence
we chose this deliberately because during the corpus analysis we noticed that most of the markers that connect large textual units can be identified by a shallow analyzer
the enforcement of this criterion reduces on one hand the recall of the discourse markers that can be detected but on the other hand increases significantly the precision
we continue investigating other weighting schemes as well
however the size of lr parser tables can be exponential in the size of the grammar because of the number of potential item subsets
the sum can be computed after completing both forward and backward passes or during the backward pass itself by scanning the chart for predicted states
this grammar will cause the earley parser to find all partial parses of substrings effectively behaving like a bottom up parser constructing the chart in left to right fashion
the elements of rl and ru are non negative since they are the result of adding and multiplying among the non negative elements of pl and pu respectively
if we did n t care about finite computation the resulting geometric series could be computed by letting the prediction loop and hence the summation continue indefinitely
thus the present algorithm fills a gap in the existing array of algorithms for scfgs efficiently combining the functionalities and advantages of several previous approaches
g has no useless nonterminals iff all nonterminals x appear in at least one derivation of some string x c g with nonzero probability i.e.
these preferences are associated with the grammatical relation
we also assume a partial order on types
this knowledge can then be shared by severm words
figure NUM an extract of the domain ontology
processing metonymy a domain model heuristic graph traversal approach
we focus here only on the link resolution algorithm
after some experimentation we chose the following scheme
thus for e2 in figure l a the donkey thai is involved depends on the farmer
statements may either i e made about concepts or about the things concepts rel r to
for clarity the events linking hierarchies of farmers and donkeys have been written as spec for specialisation
phe nlorning star is the last p int of light in the sky to disal l ear at dawn the evening star is lhe first l oi t of lighl in the sky to appear at dusk and venus is a particmar planet of the solar system
this lca ls i o l lm dcll nilion of lisl rilmt o hmss as th0 dog co i which indol cndoth i ioces or infortn lgion arc oxprcssod as indel nd nl clusler
thead constraints ate trivially satisfied or violated in preterminal rules
phone NUM NUM NUM NUM NUM NUM NUM NUM fax takeda trl vnet
given in the output of the lexical rule can be specified on the out specification of the lexical rule if the specification of c is transferred as a whole via structure sharing of the value of c
it is a yet open question whether such a theory can be encoded by weights
tgl is particularly well suited for the description of limited sublanguages specific to the domains and the tasks at hand
first they can avoid the computational complexity in dealing with infinite trees such as above
contents and messages very difficult to envisage given m will be virtually excluded from the game
figure NUM a meaning game about references of nps
another type of contextual effect shows up the following discourse
figure NUM a meaning game about propositions and sentences
here s knows something that r did not know before receiving the message
second each such smaller game is a compound game consisting of temporally overlapping meaning games
are ranked in cf u according to their saliences
figure NUM inference by s to communicate semantic content cl
we will come back to this issue in section NUM
the syn parser and the sem parser are agenda driven chart parsers
null spurious constraints which basically build representational structures
typically these are t he syntactic constraints
these subgrammars are generated from a common source grammar
NUM NUM reducing the representational overhead by separating syntax and semantics
edge id uniquely identifies a chart edge
modularizing codescriptive grammars for efficient parsing
section NUM NUM then discusses possible solutions
instead of destructively modifying the feature
in accordance with the pplr this leftmost element is promoted to the subj value of kdnnen while the remainder of the comps list of the verb governed by k6nnen identified by tag NUM is retained
the interest of such an approach was illustrated in our treatment of soes which we characterise as involving two phenomena the computation of an i sv and the resolution of a accented anaphor
in this paper we assume a definition of the fsv which is in essence rooth s alternative set that is tile set of semantic objects obtained by making an appropriate substitution in the focus position
semantics which assigns to any syntactic constituent a meaning which can be either a k term or a structured meaning i.e. a tuple of the form lcb gd where gd is krilka s i ocus semantic value and NUM is a possibly cornl iex bcus
jivcn sl an ard it ssillil lions about synt ax sll h onstoitu nts io llot exist so thai t rcb lo desired ini erprel at ion ci llliot be g eiicrated
comparison with NUM ooth and kritlm as hicnljonc d in section NUM NUM under tie alternative semanl ies al l roach a cus o cral or iie essariiy associal es with any f ocus occllrrilig in its scope
for instance in 2a b the focus operator only associates with focus so that the difference in focus between 2a and 2b induces a difference in meaning between the two utterances in a world where aon introduced paul to mary and sarah and no other introduction takes place 2a is necessarily false whilst 2b is true
however under the alternative semantics approach it will not be ruled out since the fsv of 6a provides an appropriate quantification domain for the focus operator in 6b as required by the semantic of pre verbal only it is a set of properties whose elements can be identified with the vp semantic value ax l x rn
in the given context the preferred reading of 3b can be glossed as follows it is also the case jbr NUM u that jon only read the lette r s she sent to pa uli i.e. on did n t read the letters shc sent to c g peter
extracted a utomatically and used to calcma te the similarity between the unknown wom and a
the condition of the rule yields true if the semantic representation of the destination describes more than one object
in counting instances of nouns associated with each adjective elided nouns and anaphoric pronouns were resolved manually whenever possible adding to the counts for the noun referent since we are studying the phenomenon of the adjective noun relation
disambiguation by these syntactic and semantic attributes is effectively as reliable as disambiguation using significant indicator nouns having three apparent errors in disambiguation is not significantly worse than the errorless performance of the significant indicator nouns in the NUM sentence samples
taking into account the propagation of specifications the result of the successive application of lexical rule NUM and lexical rule NUM in any order leading to state q7 or q9 bears the value on features w and y
in tliis l aper the distinguishing fea tllres aa e extracted automaticmly reflecting the characteristics of the corpus to be used
NUM translation failure if t can not translate s at all add the pair s t to t as a translation pattern
this is equivalently expressed as leave l v i v i partir l which is physically implemented as an entry of an english french lexicon
a nd modal verbs and with t tienoilielia which t t l y well be rega r led
the fact that the number feature is variable number v5 indicates that the number of the verb phrase is not specified by the sentence
the analyst can choose among these tags or by clicking on a panel of all possible tags insert a tag not in the ranked list
in this way quality control determination of output accuracy and consistency control were handled conjointly via the twin methods of sample correction and constant treebanker grammarian dialogue
NUM it ies is that the less able treebankers were also much less prolific than the others producing only NUM of the total treel ank
as with the tagging i rocess this is done by an automatic procedure with manual correction using microemacs with a special set of nlacros
even though all treebank parses are guaranteed to be acceptable to the atr grammar insuring consistency and accuracy of output has required considerable planning and effort
a smattering of such documents is included because within standard english these linguistic varieties are sometimes quoted or otherwise utilized and so they should be represented
on a more mundane bookkeeping level values for text title author publication date text source etc are recorded as well
if it is completely random the mean rank would be NUM
however there is a more fundamental limitation to the above methodology
we define on vo lj vt the binary relation derives denoted the relation symbol is sometimes
since x is an element of ps g its shared parse forest g x is not empty
methods or c to use self organising adaptation techniques e.g.
a homogeneity test was therefore performed on the corpus of each domain
an alternative strategy is to work in a bottom up direction
the top and bottom NUM texts on this fist are as follows
among these types we can notably cite tree adjoining grammars tags and linear indexed grammars ligs
the purpose of this section is to define the set of such strings as the language defined by some cfg
so the extraction of individual parses in a lig is merely reduced to the derivation of strings in a cfg
even for non cyclic grammars the number of parse trees can be exponential in the size of the input
in section NUM we used a cfg the shared parse forest for representing all parses in a cfg
a derivation is a sequence of strings in v s t the relation derives holds between any two consecutive strings
two sets of experiments were performed
including monosemic nouns precision raises as shown in table NUM from NUM to NUM NUM and the coverage increases from NUM NUM to NUM NUM
the automatic method for the disambiguation of nouns presented in this papcr is ready usable in any general domain and on free running text given part of speech tags
apart from extending lhe disambiguation to verbs adjectives and adverbs cross catcgorial relations would allow to capture better lhe relations alnong senses and provide firmer grounds for disambiguating
part of this work is included in projects NUM ta248 NUM of the basque country university and pi95 NUM of the basque government
this fully automatic method requires no hand coding of lexical entries hand tagging of text nor any kind of training process
the main procedure to resolve lexical ambiguity of nouns using conceptual density is sketched on section NUM section NUM descri bes extensively the experiments and its results
conceptual distance tries to provide a basis for measuring closeness in meaning among words taking as reference a structured hierarchical net
likewise the results for br a01 which contains short journalistic texts are hest for window sizes from NUM to NUM decreasing significatly for size NUM
the most extended approach use the context of the word to be disambiguatcd together with inl ormation about each of its word senses to solve this problem
thus it folh ws that s2 lcb did all
french and english instructions often diverge on this aspect
if procdder au remplissage du rdservoir hydraulique
i give in section NUM an overview of glose
thus on these examples the feature based subsumption account and the lcg of complement coordination constructions impose similiar feature constraints they both require that the predicate s feature specification of the complement subsumes the features of each of the arguments
in many cases all the three components for implicit communication of ironic environment are easily recognized by the hearer
predicate romove with the same arguments
fable NUM danslation time for short sentences
the following sentences cause only minor structural ambiguity
noun verb create a passive arc
the head part is designated in each pattern
article noun verb preposition proper noun preposition numeral postnominal a constituent boundary pattern is defined as a sequence that consists of variables and symbols representing constituent boundaries
table NUM l ranslation time for long sentences
with bacon chicken eggs lettuce and tomato on it
this requires many computations and results in inefficient translation
when the length of a text is i bytes it occupies l consecutive bytes in memory as depicted in figure NUM
from the viewpoint of machine learning flexible collocations are much more difficult to learn because they involve the combination of elements
the forward and inner probabilities of the states thus created are those of the first state x y1 yi lyi multiplied by factors that account for the implied eexpansions
our method replaces the parse trees with the similarity trees and thus avoids the combinatorial explosion inherent to the parsing ba sed methods
he correct use of these collocations grea l ly inlluellcc s the qua lity ofoutpttt texts
although these methods re rob is hi i assllllle rio illfol lll ttioll soltrce their outputs are just word word corresl otmences
in this section we briefly classify the types of japanese english collocations by using the material in table NUM as an example
even if the part of speech taggers make errors in word segmentation the errors can be recovered in the word chunk extraction stage
when both japan and the u s and japan and the arise from a sentence the latter is removed because the former subsumes the latter
incremental left to right computation of prefix probabilities is particularly important since that is a necessary condition for using scfgs as a replacement for finite state language models in many applications such a speech decoding
one feature structure subsumes another iff the intormation contained in the former is less specific than in the latter
when the le for k6nnen is unified with the left hand side of the pplr the comps list of kb nnen and via structure sharing the comps value of the governed verb becomes further instantiate this comps list now contains as its leftmost element a category with accusative case
the comps list of the left hand side of the pplr on the other hand rcb requires the leftmost element to carry accusative rhere are some cases of long distance passives i.e. passives which involve the complelnent of an embedded verb that at least some german speakers accept e.g.
regarding the first task in van noord and bouma s approach the sequences of lexical rules that are applicable to a given base lexical entry have to be specified by the grammar developer along with delay statements which allow goal freezing at run riffle of not sufficiently ins an fated relations
it should he noted that the granularity of sense distinctions at the ldoce homograph level eg
the next example shows the handling of a cyclic grammar
the corrector evaluated all those sentences as correct or quasi correct
a separate homonym and a synts are extreme instances of fragments
at present the linguistic information used by the corrector is not complete
for more than NUM of ill formed sentences the right corrections were found
this value of c will be denoted by c NUM
the gender and animacy of a noun are explicitly indicated in its paradigm
if this was not fulfilled generation of a distorted sentence was repeated
evaluation and thus comparisons and improvements are also impossible in chinese computational linguistics without standardized segmentation
the two major components of the segmentation standards are the segmentation criteria and the standard lexicon
they include le perfective marker and de relative clause marker
each chinese character stands for one phonological syllable and in most cases represents a morpheme
no efficient sharing of electronic resources or computational tools is possible unless segmentation can be standardized
in computational terms no serious chinese language processing can be done without segmentation
the segmentation lexicon contains a list of mandarin chinese words and other linguistic units that the heuristic guidelines must refer to
the standard is proposed to achieve linguistic felicity computational feasibility and data uniformity
hence we stipulate that the proposed standard must be linguistically felicitous computationally feasible and must ensure data uniformity
however the rule based approach has a bottleneck in that it is a hard job to add discourse knowledge when the employed nlp system deals with a larger domain and more vocabulary
the results of the first experiments showed a NUM accuracy for closed data and a NUM accuracy for open data
the htrger the value of equation NUM in two utterances gets the more plausible the local cohesion between them becomes
where flo NUM and 3t fzo f12 NUM NUM are nonnegative parameters
the experiments were carried out to decide whether one utterance and the next one have local cohesion with each other or not
r2 the speech act types are stable while the nouns and the verbs are sometimes omitted in utterances in spoken dialogues
cohesion local u r cohesion speechact speech act r speech act j cohesion endexpr endexpr endexpr
this could then be extended to include extralinguistic context in general such as interpreting the waypoint to mean the one the robot is currently facing or my right to mean NUM degrees perpendicular to the way the robot perceives the operator to be facing
given a node nd a nd its candidate viewpoi n t a pair of a relation marker rel and a wo d w the typicalness of the viewpoint is calculated as
go to offme goal etc e.g.
the test group was rotated NUM times and therefore all nouns were used as a test case
suppose that we haw data given by ills antes of the case frame of a verb automatically extracted from a corpus using conventional techniques
our experimental results indicate that for certain classes of verbs the accuracy achieved in a disambiguation experiment is improved by using the acquired knowledge of dependencies
in this paper we use case slots to mean re face case slots and we uniformly treat obligatory cases and optional cases
i would you mind if i asked you to clean up your room please
plies that violation of pragmatic principh s is not an answer to q2
in 2practically speaking whether an utterance is ironic is a matter of degree
finally section NUM suggests that our theory agrees well with several empirical findings
for example the utterance 3a explicitly expresses speaker s counterfactual emotion
an utternace implicitly displays all the three conditions for ironic environment when it NUM
note that our notion of speaker s expectaions subsumes cultually expected norms and rules
thus the degree of ironicity might t e a better criterion for recognizing irony
we can see that in terms of coverage wordnet outperforms mdl thesaurus but in terms of accuracy mdl thesaurus outperforms word net
we also extracted as our test data NUM verb no nll prep noune patterns dora the data in the same corpus which is not used in the training data
mle as its name suggests selects a model which maximizes the likelihood of the data that is NUM a rg maxp i c s p x
given the current state of the word clustering technique namely it requires data size that is usually not available and it tends to be computationally demanding this strategy is practical
in this context a model with less clusters tends to be simpler in t erms of the number of parameters but also tends to have a poorer fit to the data
in statistical natural language processing usually the number of parameters in a probabilistic and it depends on the exact coding scheme used for the description of the models
we empirically compared the performance of our method based on the mdl principle against the maximum likelihood estimator in word clustering and found that the former outperforms the latter
u the fut ttre hopefillly wit h target training dat a size we plao to construct larger thesauri as well as to test other clustering algorit hms
figure NUM shows all example thesaurus for the NUM most frequently occurred nouns in the data constructed based on their appearances as subject and object of roughly NUM verbs
this model is similar to the model nsed by stochastic cfg
let us consider dependency parsing in t his framework
string and these l robabilities sunl to one
three new probabilistic models for dependency parsing an exploration
we can implement our data structures in such a way that each of the primitive access operations that are executed by the algorithm takes a constant amount of time
let t e s t a node of t is called leftmost if it does not have any left sibling a root node is a leftmost node
in what follows we construct a dta that detects each subtree of an input tree that is equivalent to some tree in lhs r
in fact our definition of the relation and of the underlying operator has been inspired by similar definitions in the nce formalism
we can then conclude that the running time of algorithm NUM is o iti pt t log t t
consider each instance of the membership of a node n in a set rule i and represent it as a pair n i
we write qp to denote the tree obtained starting from q by excising s and by letting the root of qc be the new i th child of hi
then the parser iteratively checks an ordered sequence of tree transformations for application to the initial parse tree in order to derive the final parse structure
as shown in this figure with tar the context information also modifies the NUM redicate like l y default in l oth senten es NUM and NUM
in this ease however they are itemized phrases and by reference to NUM they all be identified as supl lementary w rb phrases to be attached to NUM
the a cura y of syntactic analysis m y l e improved by refinement of the ontext nn del in tlt second step of the procedure
in a valid structural description each structural position may be filled with at most one input segment and each input segment may be parsed into at most one position
i would also like to thank david i iaussler clayton lewis mark liberman jim martin and alan prince for useful discussions and three anonymous reviewers for helpful comments
each such category corresponds to an incomplete edge in normal chart parsing having a table cell for each such category eliminates the need for a separate data structure containing edges
the other two indices a and c indicate the contiguous substring of the input string covered by the partial description contained in the cell input segments ia through ic
the loop iterations stop when none of the overparsing operations is able to fill a cell each proposed partial description is less harmonic than the partial description already in the cell
the first test set named bc50 consists of NUM NUM occurrences of the NUM content words that occur in NUM text files of the brown corpus
the second test set named wsj6 consists of NUM NUM occurrences of the NUM content words that occur in NUM text files of the wsj corpus
since wordnet orders its senses such that sense NUM is the most frequent sense one possibility is to always pick sense NUM as the best sense assignment
in contrast we used exemplar based learning where the contributions of all features are summed up and taken into account in coming up with a classification
previous research on wsd tend to be tested only on a dozen number of words where each word frequently has either two or a few senses
lexas is trained on the remaining NUM sentences and then tested on a separate test set of sentences in each trial
to our knowledge very few of the existing work on wsd has been tested and compared on a common data set
in summary when tested on the noun interest lexas gives higher classification accuracy than previous work on wsd
the size explosion is not quite as great here but the resulting transducer is still large compared to the original rule file which only requires NUM bytes of storage
another important point is that each decision tree considered here has the property that its predictions specify how to rewrite a symbol in context in an input string
in this section we presume that one has already trained a tree or set of trees and we merely remind the reader of the salient points in the interpretation of those trees
the full set of realizations at this node with estimated non zero probabilities is as follows see table NUM for a relevant set of arpabet ipa correspondences phone probability log prob
each leaf node can then be treated as a separate rule where the left and right contexts are constructable from the decisions made traversing the tree from the root to the leaf
we have presented a practical algorithm for converting decision trees inferred from data into weighted finite state transducers that directly implement the models implicit in the trees and we have empirically verified that the algorithm is correct
to start with we know that this tree models the phonetic realization of aa so we can immediately set c to be aa for the whole tree
thus we will use the quantitative criterion described above
word base form morphological analysis verb 1st person singular present indicative vlspi verb 1st person singular present subjunctive vlsps verb 2nd person singular present imperative v2spm verb 3rd person singular present indicative v3spi verb 3rd person singular present subjunctive v3sps table NUM morphological analyses of the word marine
e.g. in figure NUM the magic s rule magic s finite ssem magic sentence decl ssem
a possible solution to this problem is to couple magic rules with the modified version of the original grammar rule that instigated it
the first sentence introduces the dynamic individual xa as follows6
the antecedent for the vpe is love his cat
this will arise in the cases of sloppy identity exalnined below
we simply permit sloppy identity for any proform whenever the anliece le nl
the pronoun his is equivalent to his0 and dins refers to tile discourse center
in addition verbs often participate in broad focus readings and fllrther research is needed to account for the observation that broad focus readings are only available in canonical word orders
the cb of an utterance is delined as the highest ranke l element of the previous u tterance s cf list that also occurs iu the curren utterance
however we can also use focusing in or null der to contrast one item with another and in this case the focus can be discourse old or discoursenew e.g.
NUM as seen in figure NUM brand new discourse entities are found in the ipv position but never in other positions in the sentence in my turkish corpus
languages such as catalan czech finnish german hindi hungarian japanese polish russian turkish etc have much freer word order than english
within the comment we tind the focus the most information bearing const itnent in the senten e and the ground the rest of the sentence
if an item that is not in the discourse model is nonetheless realized as a definite np in the source text the speaker is treating the entity as discourse old
in the machine translation task from fnglish into a free word order language it is crucial to choose the contextnally appropriate word order in the target language
null the above technique can be applied for each periodic node in a critical rule and for each critical rule of g
for each dead pair only the test in the head of the for loop is executed taking a constant amount of time
each such arc for a particular class points to the most probable pair of this same class
this section compares different n type and s type transducers with each other and with the underlying hmm
the extraction of the union use of extended middie subsequences is performed in a similar way
table NUM compares the tagging accuracy of different transducers and the underlying hmm for different languages
NUM NUM a improves the relation between tagging accuracy and the size of the transducer
a middle subsequence cm starts immediately after an unambiguous class cu has any number incl
we also hope to improve the n type model by using look ahead to the following tags NUM
e.g. every arc leading to the state of cl t12 is labeled with cl t12
this section presents a method that approximates an hmm by a transducer called s type approximation NUM
we exploit this sparseness in rule applications to derive an algorithm two to three orders of magnitude faster than the standard parsing algorithm
the slot pos ition in combination with the constraint NUM requires that the postmodifying phrase directly tbllows the np fi r which this frame is defined
as a consequence syntax is kept to a minimum and partial only clause segmentation verbdetection and ni pi demarcation is considered to be necessary
endlemma c the type lexicon a knowledge base with an entry for each of the NUM surgical deed subtypes and NUM neutral subtype
the lunction of the module is twofold NUM generation of a list of words which m e likely to be medical terms and cen concepts
the surgical deed concept is classified into NUM subtypes among others cs remove cs close cs create cs close cs install cs make appear
the general rule for the guessing of sm gical deeds is each verb that has a semantic link with a cen concept is a surgical deed concept
ex NUM input enkele fragmenten discus worden nog verw derd dan worden met een beiteloe de osteofytaire randen van de dekplaat weggenomen
by making educated guesses the system moreover has a possibility to expand its own lexicon of medical terms so to be able to cope with new texts
treating this form requires calculating the effects of actions
the effects are taken to be cah nlated recursively
in the case of this example the seed looks as follows magic sentence decl buys john a book mary
understanding snch dialogue phenomena reqnires handling effects and preconditions
table NUM shows how the training corpus provides coverage for n gram genotypes that appear in the test corpus
the lexical differences reported in the next sections will be systematically evaluated from a controlled language perspective
NUM lexicalise the arguments xl xn and link the resulting lexemic structures to v
our sentence realiser glose is based on meaning text theory mtt NUM
the relations between nodes represent deep syntactic relations which are defined as abstractions over superficial syntactic relations
for this kind of differences the french and english lexicalisations rely on the same basic mechanisms
operator verb constructions have already been studied from a machine translation perspective NUM
items another important lexical difference concerns the specificity level of each element of the bilingual pairs
by contrast the selection of denominal verbs involves mappings between several concepts and a single lexeme
by contrast an attribute of category manner can be combined with both event and object perspectives
sentence 9e is not acceptable since specific verbs have to be prefered when available
a methodology is presented in order to optimize the construction of a restricted training corpus for developing taggers
since that fllnt tions are general we may try to lind sui i ort flmctkm more speciiic tbr our t rol h m
usual support flnmtions are based on coinputing for each constraint r involving vi t rcb tile constraint influence inf r c
NUM application to pos tagging in this section we expose our application of relaxation labeling to assign NUM u t of speech tags to the words in a sentenc e
any rehttionship between any subset of words and tags may NUM e expressed as constraint and used l o feed th algorithm
only if we have an upper bound for the nun ber of iterations i.e. convergence is fast or the algorithm is stopped after a fixed number of iterations
actually what we arc loing is changing our convergen e criterion to one more sophisticated than sto NUM when dlere are no inore changes
in lcg semantic interpretation and long distance dependencies are handled independently of the feature system so agreement phenomena seem to be the major application of a feature system for lcg
this paper compares the consislency based account of agreement phenomena in unification based grammars with an implication based account based on a simple feature extension to lambek categorim grammar lcg
as we remarked earlier in lcg predicates are analyzed as directed implicational formulae and the argument features required by a predicate appear in the antecedent of such formulae
kim vp v became lap wealthy and np a republican now consider the coordination in 2b
similarly we decompose the four nominal cases in german into the subcase features obj abbreviating objective and dir for direct as follows
NUM a er findet und hilft miinner he find acc and help dat men acc b er findet und hilft kindern he find acc and help dat children dat c
because the lcg account of agreement has subsumption built in the coordination rule merely requires identity of the conjunction and each of the conjuncts
this example brings out one of the fundamental differences between the standard treatment of agreement in unification based grammar and this treatment of agreement in lcg
such overspecified or inconsistent features may seem ad hoc and unmotivated but they arise naturally in the formal framework of morrill s extended lcg
note that in practice actual performance is improved by the sparseness of the translation matrix
collipoileiils o iiol fit well wheii viewed as objccls
seliteilce ca llilol t e NUM hough of a s physica l
select right meaning NUM enclosed inside curly brackets lcb and rcb is a
a development process of a muld modm dra wing tool a long with the multi roods l method
int vfaces becomes much larger than for any single moda NUM int erfitces
io cha llge t hc ihouse positiotl frol l the ciiliivihs to it
then the system must recog nize in which order tile poiucs and the anaphoric references occur
io s in computer i rogi aniiiiing during the past leca de
in the following example speaker a is explaining an incident in which she was asked a difficult favor and speaker b is responding expressing her understanding of a s difficult position
zero pronouns are ellipsis of obligatory ca ses which very frequently appear in japanese sen null tences
to capture pragmatic constraints we have paid our attention to conditionals which occur very frequently in instruction manuals
in this paper however we will use the term subject to denote a main participant of the sentence
since the assumptions are introduced by the speaker the matrix clause is to describe speaker s expectation or desire
these examples show that the expressions to and are impose some constraints on the referents of subjects of the sentences
therefore the semantic representation would include some undetermined parts which would be fixed by other kind of information including context
null we do not commit ourselves to the domain specitlc knowledge but use some ontological knowledge in general manuals
for example the col respondence of objects in the mamlal sentences to the objects in linguistic coustra ints
in a complex sentence with the connectzve particle to orreba the matrix clause does not empress user s volitional action
one of ways to get rid of this situation is to adopt some knowledge which hardly depends on some particular domain
this process relies on conceptlexeme mapping structures integrated in the lexicon and which represent elementary transitions from conceptual structures to lexemes
then i discuss brieily in the next section the corpus analysis and its role in the design of the multilingual generation system
the komet grammar provides linguistic resources including intonational options
how is choice in the basic mood options constrained
w rdest du das fenster schlieflen bitte
you re not supposed to open the window o
tiffs approximate calculation is likely to result in an overestimated correlation when there is ambiguity in pairing jw c w and eu g c ew as occurs in fig NUM a
although one occurrence of a word may not give a sumcient context to chm acterize the word accumulating all the contexts in which the word occurs throughout the text allows the word to be distinguished from the other words in the same language text
the resultant co occurrence set i s expressed as c w lcb w f i NUM n rcb which shows that word w co occurs with word w times
oo occu on e ot or i oa u at oo o corre a oo i co occ rrooco each japanese word NUM NUM each english word correlation for each pair of set of words ffor each sentence set of words for each sentence ual dicti i co occurrence data extraction fig NUM method for extracting word correspondences
while co occurrence in a k word window may produce better results when a sentence in one hulguage corresi onds to a sequence of lwo or more shorler sentences in tile other language it is difficult to determine an appropriate wdue of k because word order differs considerably between japanese and english
tile underlined numerals in the following pair of sentences is an example of a retbrence number g b z l NUM NUM NUM a xjjt bilingual corpus that m e actually extracted
the verbal entry load subcategorizes for a single np subject and ni and pp complenmnts NUM
for the moment the database contains for the major part simple wordforms
this reslflt indicates that small ius are frequently used
thus circumstance is useful for the incremental strategy
the pause length limit was NUM see
this plan is written as rl
these were ewduated by an utterance simulation experiment
this model has been implemented in common lisp
the rule base can be subdivided into NUM i ndependent rule sets
section NUM and the implementation of tile major data structures cf
using approach NUM with hand crafted lexicons has the disadvantage of being expensive to create in null essentially hand crafted disambiguators
a specific mechanism selects the appropriate ruleset to be triggered
again straight comparison would not be trivial as e.g.
the anaphora resolution algorithm generates the following analysis for the first italicized pronoun
step NUM during this step we decide on the attribute the result might be old
anaphora for everyone pronominal anaphora resolution without a parser
overall these considerations lead to two conchl
we are currently mfinin g the existing expletive patterns for improved accuracy
the candidate set illustrates several important points
while adaptations to the input format and interpretation procedures have necessarily addressed the issues of coping with a less rich level of linguistic analysis there is only a small compromise in the quality of the results
our evaluation indicates that the problems with the current implementation do not stem from the absence of a parse but rather from factors which can be addressed within the constraints imposed by the shallow base analysis
the result of the ranking is that internet access device the candidate which satisfies the highest weighted salience facto1 subl s is the optimal candidate and so correctly identified as the an tecedent
we are thus lead to the approach of approximating tile tal get joint distribution by such a simplified model based on corpus data
if they are anaphors they are considered to be unbound variables and result in unfilled roles in the cs
in the conceptual analyser the general disambiguation module gdm deals with other ambiguities like prepositional phrase attachment
after the first sentence it confirms or rejects the predicted focus taking into account the results of anaphor interpretation
NUM vulcan made i s initial investment in telescan in may NUM
an ee is the unit of processing in our resolution algorithm instead of the sentence
prr could refer to intrasentential antecedents in simple sentences such as in those of sentences NUM and NUM
having mentioned this particular case of prr in initial ee we now expand on the whole algorithm of resolution
the proposed algorithm has been tested and implemented as a part of a conceptual analyser mainly to process pronouns
in the following subordinate phrases like relative or causal sentences will be also considered as embedded ones
in condition NUM b denotes actions which disable an action a of condition NUM
a NUM rcb rcb and its illocutionary act is lnfl rm
this im null example NUM candy baked a pizza to satisilg her hunger
even so because the grs mm r generates a large number of parses for each sentence s it is not feasible to rau the parses exhaustively
another feature of this type is det pos which reveals concerning a noun phrase whether it includes a determiner phrase and if so what type
for instance when we predict whether a constituent has ended we ask how many words until the next finite verb the next comma the next noun etc
finally a statistical procedure is described for converting less detailed into more detailed treebank for use in increasing parser accuracy via much larger training treeb nlc
effectively on tagging with our NUM tag syntax only tag subset approximately one word is syntactically mistagged every two sentences leading to an increased error rate at exact syntactic match parsing
cross indicates percentage of test data sentences whose top r nlced parse contains NUM instances of crossing brackets with respect to the most probable treebank parse of the sentence
given a set of models for estimating the probabilities of parse steps the problem of predicting a parse reduces to searching the space of possible parses for the most likely one
NUM each model uses as input the answers to a set of questions about context designed specifically for that model by our team grammarian using the language described in section NUM NUM
at one extreme a noun phrase may not have been modified at all so far and so other things being equal it is a prime target for post modification
base used for parse prediction allows the system to parse in a domain general totally open vocabulary setting and to output highly detailed semantic as well as syntactic information for sentences proccessed
this theory howew r colnpletely fails to explain many ironic utterances
both methods have been fully implemented
we distinguish between initial and middle subsequences
we ask all decision tree questions in our treeb n conversion models that we do normally in parsing with the atr english grammar
all above results at obtaine d
so they are often stoplmd after a few iterations
table NUM best relaxation results using every combina
for the labels of the other variabh s
this is called the support function
the first formula combines influences just adding them
results ob null tion of constraint kinds
table NUM results achieved by conventional tuggers
combining information in a llack off h ierarchy
experiment with different stopt ing criterions
this requires system tuning and is often dependent on the application
from raw text a human tagger could manually disambiguate texts
having a concise set of tags is therefore a priority
this class unlike the other two is productive
NUM will generate two lexical entries with at1 identical i iion string
however several of them use additional external powerful mechanisms to simulate type inference
the described system has been implemented and has been integrated with the speech recognizer janus
this guarantees that only those grammatical lexical entries are infered that are needed for efficient parsing
the operations are used to formulate rules that control the interaction between blackboards and agents
for each rule to be evaluated the set of variable substitutions is calculated
in the same way the other variables in the rule are looked up
this leads to a new type of semantic lexicon corv lzx that supports underspecified semantic tagging through a design based on systematic polysemous classes and a class based acquisition of lexical knowledge for specific domains
consider this example from the brown corpus a long book heavily weighted with milltary technlcalities np in this edition it is neither so long nor so technical as it was originally
for instance wordnet assigns to the noun book the following senses the content that is being communicated communicatiofl and the medium of communication artifact
door gate window are not normally addressed simultaneously in discourse rather one side of the object is picked out in a particular context
here we encounter mot quot syn form which resolves to mor passive participle in virtue of the embedded global reference to syn form at word3
evaluable paths may themselves be global as in our example or local and their evaluable components may also involve global or local reference
NUM thus a lexical rule for direct wh questions may be a variant of that for indirect wh questions similar sharing components but not identical
NUM for this discussion we have already adopted the convention that both nodes and atoms are simple words with nodes starting with uppercase letters
it is up to some component external to datr that makes use of such complex values to interpret it as a two member set of alternative forms
putting aside considerations of functionality for the moment we see that in datr both the second and third of these options are employed
computational linguistics volume NUM number NUM inheritance statement so that ultimately the consistency of the set of extensional value statements is assured
we assume that the morphology of present tense forms is specified with paths of five attributes the fourth specifying number the fifth person
such value descriptors will include inheritance specifications that allow us to gather together the properties that wordl and word2 have solely by virtue of being verbs
very strong correlations appear between particular choices of semantic relation and syntactic form on the one hand and the appearance of discourse markers and or a strong bias towards a particular rhetorical interpretation on the other
we report here on work which addresses the message to syntax mapping in the context of automatic generation of instructional texts the kinds of texts found in the procedural parts of manums or information leaflets pharmaceuticm products
in example NUM advantages of this approach over the inherent limitations of a translation based approach to producing pressing the correct keypad numbers has the automarie effect of dialing the numbers of the mercury authorisation code
the gerundive is strongly marked for generation and in the rare cases it is used in enablement is restricted to a single semantic role expressing enabling rather than enabled the only french expression so restricted
null a particularly important element to emerge is the language specific nature of the choice of rhetorical relation a notion which we express for the moment in terms of l st style rhetorical relations of
the analysis represents a careful but intuitive interpretation of what rhetorical relation would be retrieved by a native speaker of the language front the particular combination of syntax discourse marker and content
example NUM front the instructions for a home photographic slide viewer presents the enabled action prolonged viewing first and describes to tile user what must be done to facilitate it
if appears exclusively with the e1 first presentation and and then followed by follow x by and now only ap pear with the ing first ordering do commas
in such a case should not be recognized
figure h effects of complex action
the maximum likelihood estimate of f ri a b c d is c r a b c d NUM fi rl a b c d c a b c d we can now make the following approximation
a dynamic programming algorithm is used if two proposed constituents span the same set of words have the same label head and distance from with the punctuation rule described in section NUM NUM NUM is model NUM with pos tags ignored when lexical information is present NUM is model NUM with probability distributions from the pos tagger
otherwise in a phrase like the securities and exchange commission closed yesterday pre modifying nouns like securities and exchange would be included in co occurrence counts when in practice there is no way that they can modify words outside their basenp
s np sml h vp announcedl iq smah nplmu nt j presidmt pp of vbd annoumzdi np fesignatian np yeuaerday nn t p i nn nn
for example c a b c d a is the number of times a b and c d appear in the same sentence at a distance a apart
in practice this is a good approximation because most basenp boundaries are very well defined so parses which have high enough p bis to be among the highest scoring parses for a sentence tend to have identical or very similar basenps
informed introspective analysis aided by a perusal of corpora seems a surer way toward rule based formulations
the mutual relevance of nouns and adjectives that permits sense disambiguation is concept specific rather than word specific
when her right hand was incapacitated by the rheumatism sadie learned to write with her left hand
certainly some nouns are strongly associated with particular senses of some of the adjectives that modify them
nouns indicating the antonym specific senses of these targets were then extracted by statistical analysis of their sense preferences
we often say of a person that he looks young for his age or old for his age
one hundred and eighty one adjective noun pairs occur more than once NUM with a total of NUM occurrences
accordingly physically supports is a semantic attribute of some indicator verbs for the not heavy sense of light
some of these sentences have more than one such co occurrence so these sentences yielded NUM total co occurrences
the specificity of man and house to particular senses of old is typical of nouns in these subcorpora
from its head child the vp
NUM head modifier relationships are now extracted
NUM NUM some further improvements to the model
for the purposes of our model
this is an area for future research
figure NUM diagram showing how two constituents
to find out fixed collocations we evaluate stochastic similarity of the chunks
in this paper we extend ikehara s method to handle word based n grams
for any pair of chunks in a japanese sentence compute mutual information
in the present implementation the system combines three word chunks at most
so a lapse of memory el crates often in text coml rehension arid rotecl s the text rein dee incoherence
el oh renc in knowledge rel res mtation for natural language discourses and to pose tim first foundations tk r formal rel re sentation
we have to pose the properties of such relations depending on if they refer to an action or a state a relation is defined by an action schema or a state schema
relation between an object and its constitive parts
though the incoherence of a text may result from a lot of phenomena we restri t ourselves in this communication to incoherence stemming from negal ions
a fact preserlted as a type is negated as such and related to extensional objects or the converse NUM hc doq is not a stupid animal but fctcr s is
as indicate l l reviously only the intensional objects the types haw a h gical behavior they represent generm knowledge valid in the discourse
l he individuals of the knowledge base are objects
therefore lcb he le will both unify with and be subsumed by the input datnre structure of the ijpli lcb
i inally the paper discusses computational consequcnce s of at plying lexical rules under subsuml tion
german exhibits two types of passnes personal passives as in lb and impersonal passives as in b
it turns out that the conclusions reached in this paper can be easily integrated in the general framework that these authors provide
also conta ins possibly cinl t y list o non w rl a l
therefore the comps list of ksnnen does not subsume the comps list on the left hand side of the pplr or vice versa
finally we will consider the computational implications that the adoption of lfypothesis b has for the processing of lrs in a computational system
NUM t is worth pointing out that the importauce of subsunlption tt s been noted for other linguistic phenomena as well
the current set of NUM features was sufficient to always discriminate examples with different parse actions resulting in a NUM accuracy on sentences already seen during training
the correct operations ratio is important for example acquisition because it describes the percentage of parse actions that the supervisor can confirm by just hitting the return key
the transfer and generation modules were developed and trained based on only NUM sentences so we expect a significant translation quality improvement by further development of those modules
while that percentage is certainly less important than the accuracy figures for unseen sentences it nevertheless represents an important upper ceiling
table NUM summarizes the evaluation results of translating NUM randomly selected sentences from our wall street journal corpus from english to german
a transfer module recursively maps the source language parse tree to an equivalent tree in the target language reusing the methods developed for parsing with only minor adaptations
using our knowledge of similarity of parse actions and the exceptionality vs generality of parse action groups we can provide an overhead structure that helps prevent data fragmentation
the knowledge acquisition process is laborious
NUM since in computational systems in contrast to the general theoretical case we only need to ensure transfer for the properties actually specified in the lexical entries of a given grammar some of the distinctions made in the signature can possibly be ignored
null as shown in table NUM the final corpus sample is made up of NUM examples all of which have been coded for the features to be discussed in the next two sections
consider a hypothetical multimodal dialogue system that was constructed according to the above guidelines as illustrated in figure NUM
finally we presented our ongoing project to make designing constructing and evaluating new dialogue systems faster and easier
control for the search can be governed by the domain dependent characteristics of the subproofs
the high level architecture we envision uses messages to communicate content and events to describe meta and control information
the system can respond with debugging or tutorial information presented as a combination of speech text and graphics
once the structure of the system is settled a variety of desirable behaviors for realistic dialogue can be programmed
the prolog theorem proving system provides a natural means for encoding and using the user model without major additional mechanisms
the complexity constants cline og etc for generation are learned and continuously updated during normal operation
in figure NUM we see that the learning algorithm can be inserted between the dialogue processing and output generation modules
most o f the processing time was spent on parsing
principar attempts to construct a parse for the full sentence
one of the purposes of lexical rules is to recognize relevant semantic entities
the last training tests was conducted just before runnin g the formal test
then st templates are filled according t o the contents in the database
information is extracted from the dependenc y trees by a subtree pattern matcher
pie got NUM recall and NUM precision for the walkthrough article
the company fallon mcelligott is incorrectly recognized as a person
a separate program is used t o resolved the differences
james chairman and chief executive officer of mccann erickson and john j
the subsequent discussion of the constraint in delimiters those tags are listed which mark the boundary of context conditions
the number of word forms with more than one reading is almost equal in both corpora slightly over NUM
in context conditions reference can be made to any of the features or tags found in the unambiguous reading e.g.
this is because in both of the cohorts there is only one such reading which refers to the same noun class
NUM he risk of wrong interpretations decreases substantially by first disambiguating noun phrases and other smaller units
in target is defined the concrete morphological tag or sequence of tags to which the operation is applied
any context may also be negated by placing the key word not to the beginning of the context clause
this can be done by adding the key word link to the context whereafter the new context follows
in analyzing word forms the system applied utilizes swatwol a morphological parsing program based on two level formalism
solutions tbr resolving different types of ambiguity are presented and they are demonstrated by examples fi om corpus text
we focus on a qualitative evaluation of space efficiency rather than on providing results for the test grammar since the space efficiency of the covariation encoding relative to the expanded out lexicon is dependent on several properties of the grammar the number of lexical entries in the lexicon that can undergo lexical rule application the size of the lexical entries and the number of lexical entries belonging to a word class
as a consequence the states q8 q15 q11 q18 q4 and q12 are no longer reachable and the following transitions can be eliminated as well lcb NUM q8 q8 NUM q8 q15 NUM q11 q11 NUM q11 q18 NUM q4 q4 NUM q4 q12 rcb
in this paper i have tried to give evidence for three claims namely that thought is not completely specified at the onset of lexicalization that there is feedback from the lexical to the conceptual component and that the dictionary plays a flmdamental role in guiding and potentially modifying non linguistic thought
of course the tree can be built in different ways top down bottom up or by combining both methods NUM for related appl oaches on NUM i believe that the way how the tree is built depends on whether tile speaker has at the onset of message planning a clear picture of the object or scene to describe or whether he has to build it from scratch
suppose we wanted to produce the following sentence clen the old man saw the little boy drowning in the river he went to his canoe in order to rescue him figure NUM here below can be considered as the underlying planning tree
the process of finding words for conceptual structures can be viewed as lexically nrediated hence indirect structure mapping conceptual li agments ue mapped onto words via the lexicon see figure NUM the latter serving as an interface between thought concepts and language words
in the analogical framework the translation system is equipped with a large database of pre translated example pairs in which the best example that matches the input expression is selected and used for generating an appropriate target language expression
in this framework the task of an the spoken language translation system can be seen as follows given the speech recognizer output the system must recover the closest example available in the example database figure NUM
a new categorization of spoken language phenomena into essentially non meaningful speech errors and purposeful natural speech properties is introduced and the manner in which natural speech properties convey pragmatic information is described
we refer to the spoken language phenomena that are non communicative by products of the speech production process as speech performance errors and to the phenomena that serve a communicative function as natural speech properties
for example while many languages have hedges similar to nanka and many languages include means to invert subject and predicate only few languages include dcletable casemarkers such as zoo or sentential particles such as yo and ne
by analyzing each operator for its pragmatic effect we can obtain a translation that preserves the speaker s pragmatic intentions well it s sort of difficult is n t it to reject something like that
natural speech properties that carry the information related to discourse include word order variations inversions right or left dislocations and filled pauses and hedges used as a floor holding device to signal the listener not to take her turn
for example if we consider a conversation between two persons who meet for the first time at a party and extract only propositionally meaningful chunks and translate them the result will resemble an interrogation rather than a pleasant conversation
data struci urc the forward looking centers and define s
since we need as we will show below a representation language which is at least as expressive as the language of first order predicate logic for an adeqm te representation of discourse meanings an inconsistency test is not computable anymore if a classical sound and as far as possible complete calculus is used for the test the underlying problem is simply undecidable
since the termination behavior of such a system is without any further empirical evidence not in any way correlated with our cognitive capabilities and without any further formal evidence not in any way correlated with the behavior which we would expect if the disambiguation problem were nevertheless decidable we have to rule out these approaches from a scientific point of view
and some sister and married NUM sister and married nothing although NUM contradicts the tbox representation NUM of NUM it is not possible to use back to establish this inconsistency incoherence since back does not allow the conjunction of roles in the abox cf
if we abbreviate physician by p sister by s and married by m and use clause set notation each conjunct of the matrix is represented as dm set of its disjunctively connected litenjs tt e unsatisfiability of NUM and NUM can be shown since there is a resolution refiltation depicted as a
eine schwester rail der sie nicht w xheiratet sind the sister reading which is expressed in english by some physicians have a sister to whom they are not married since NUM implies for physicians who NUM aw a sister that they are not married to her
NUM in order to be able to specify the incompleteness of our inference machinery in terms of a resolution logic let us in the following assume that mp and the discourse is given in skolem conjunctive form scf i.e. as two uniw rsally quantified formulas whose matrices are in conjunctive normal form
the presumption that human sentence processor uses a push down sl ack is challenged by the contrast between cross serial dependencies in dutch e.g. figure 4a and center embedding sentences in german e.g. figm e 4b since the cross serial dependencies are much more ditficnlt to handle with push down stacks
we propose th3t the lmrposc of extr31 osition is to m3ke NUM sentence easier to mlderst3nd
n the next three sections we will show that the definition of structural comph xity
l here are several reasons why the notion of strhctltl a coml lexity ix tlseful
an up feature is propagated up to its parent branch when building the feature structure see figure NUM
they are classified as being ordinal or cardinal numbers and are presented as words to the following networks
on the contrary our parser must learn the form of tile output provided by a unitication based parser
for clarity tile examl le sentences in this paper are among the simpler in the training set
feaspar is trained tested and evaluated with the spontaneous schednling task and compared with a handmodeled lrparser
these deviations are manageable and low in number when analyzing written language but not for spoken language
then the networks are trained independently of each other allowing for parallel training on several cpu s
for every feature value at a certain chunk level if there exists a word such that given this word in the training data the feature value occurs in more than NUM of tim cases
for efficient sentence analysis in particular it is necessary to disambiguate the results of morphological analysis before they can be passed oil the parser
part of such processes take place also in verbal extensions whereby the quality of the stem vowel s defines the surface form of the suffix
the main factors of ambiguity in this language group can be traced to the noun class structure on one hand and to the bi directional word formation on the other
karlsson NUM it leaves ambiguity within readings themselves in the form of underspecifica ion and it has to be resolved later in any case
in order to simplify disambiguation fixed phrases idioms multi word prepositions and nonambiguous collocations are joined together already in the preprocessing phase of the text e.g.
for describing verbs there are a number of consecutive prefix and suffix slots which may or may not be filled by morphemes
swahili does not seem to fully support this hypothesis although the numbers in table NUM and NUM are not directly comparable with results of other studies
yet the overall result has to be considered promising given that the parser is still under development and that the rules are almost solely grammarbased
about one fifth of tokens are precisely two ways ambiguous and the share of three ways and four ways ambiguous tokens is almost equal about NUM
karlsson NUM NUM karlsson 1994a 1994b karlsson et al NUM voutilainen et al NUM voutilainen and tapanainen NUM tapanainen NUM
null the vast majority of constraints are selection rules for resolving ambiguity based on homographic noun class agreement markers lit is possible to resolve most of this ambiguity by using contextual information
the test corpus also manually tagged contained NUM NUM words
they show that the performance based on trigrams is NUM NUM
the more tags the harder the estimation of probabilities and the sparser the data
then the first two classes must be exhaustively covered since their number is relatively small
we are in the process of improving the tagger performance in refining rules and biased costs
NUM analyze morphology and morphological features in order to evaluate the ambiguity of the language
a genotype decision is the most frequent tag associated with a genotype in the training corpus
it is striking to notice that these NUM genotypes represent almost NUM of the corpus
the contraints can be NUM accurate or describe the tendency of a particular tagging choice
figure NUM shows two corpora of different sizes and the number of words each tag contains
to that end the command oriented interface functions in eucalyptus consist largely of calls to the base functions underlying the koalas gui
the full language also contains type NUM conditions of the form NUM a ll ln indicating that NUM in are contributed by a single sentence etc cthenlj li e ps and ifli lj lk e c then lj li lk li e ps NUM
on a final note the remarkable correspondence between lfg f structure and udrt and qlf representations the latter two arguably being the major recent underspecified semantic representation formalisms provides further independent motivation for a level of representation similar to lfg f structure which antedates its underspecified semantic cousins by more than a decade
xn i i i i n try tr2 tr the scaffolding which allows us to ire construct a f structure from a udrs is provided by udrs subordination constraints and variables occurring in udrs conditions NUM the translation recurses on the semantic contributions of verbs
variable bindings must instead be handled locally i.e. each formula in the database will carry with it a context indicating bindings that have been made in its derivation
although it is appealing from a logical point of view to include such operators their use is not motivated in grammar
it is standard in categorial calculi to include also a product operator enabliug matter like addition of substructures e.g.
this l al n presenl s a deduction reel hod for impli a t
re cording proof inform l ion a s a basis for ensming correcl int crelming
the compilation inethod involves identifying and excising such subformulae thereby simplifying the containing formulae and including them as additional assumptions
for those indi e s that it requires hay l e m inv lved in de riving the argument
such linear formulae can be used with any linear deduction method given the trivial additional task of unifying variables and constants in the string position labels
with these enhancements the general replacement expressions are more versatile than two level rules for the description of complex morphological alternations
NUM all empty upper of type are trans null formed into type
for the sake of completeness we also define the inverse operators and and the bidirectional varian and
here we might have to replace NUM NUM and la l where NUM is part of both substrings
that is why only one a could be replaced and we would get two alternative lower strings xbxax and xaxbx
where a is replaced by b only when occuriug between x and y or after v or before w
the table below describes the types of regular expressions and special symbols that are used to define the replacement operators
as we shall see the main reason for this is that the ou approach makes minimal assumptions about the role syntax plays in determining the fsv
where np vp represent the meaning of np and vp respectively and t ks v stands for the focus semantic value of the vp
in the standard case i.e. the case where the focus is prosodically marked this quantification domain of focus operators is usually identified with tire fsv of the vp
in this paper we extend the unification based approach to cases which are often seen as a test bed for focus theory utterances with multiple focus operators and second occurrence expressions
focus is then seen as introducing a free variable whose value is determined by the current context and is filrthermore constrained to be an element or a subset of the fsv
for instance in the case of soes we saw that the equations characterising the deaccented anaphor help determine the unidentified fsv of the utterance containing the unmarke i focus
for our purpose the following characteristics are particularly important given rooth s definition of the alternative set a focus operator associates with any tbcus occurring in its scope
null fortunately in our case we are not interested in general unification but we can use the fact that our formulae belong to very restricted syntactic subclasses for which much better results are known
in this section we show that the itou approach f wourably compares with i ooth s and krifka s anmysis in that it correctly generates interpretations which these two theories fail to yield
this section presents a method that approximates a lst order hmm by a transducer called n type approximation NUM
11ongoing work has shown that looking ahead to just one tag is worthless because it makes tagging results highly ambiguous
NUM with subsequences of frequency NUM from a training corpus of NUM NUM words sec
soiiic delaying inechanisnl or some exl erision
unlike prediction the completion step still involves iteration
core slots of frames are surface and lexical form syntactic and semantic category subframes with syntactic and semantic roles and form restric1the lexicon provides part of speech information and links words to concepts as used in the kb see next section
in another test we deleted all NUM features relating to the subcategorization table and found that the only metrics with degrading values were those measuring semantic role assignment in particular none of the precision recall and crossing bracket values changed significantly
the following representative examples for easier understanding rendered in english and not in feature language syntax further illustrate the expressiveness of the feature language the general syntactic class of frame NUM the third element of the parse stack e.g.
we therefore propose an approach to parsing based on learning from examples with a very strong emphasis on context integrating morphological syntactic semantic and other aspects relevant to making good parse decisions thereby also allowing the parsing to be deterministic
while the loss of a few specialized features will not cause a major degradation the relatively high number of features used in our system finds a clear justification when evaluating compound test characteristics such as the number of structurally completely correct sentences
for each of the NUM sub tests a varying number of sentences from the other blocks is used for training the parse decision structure so that within a sub test none of the training sentences are ever used as a test sentence
in parsing only a very small number of features are crucial over a wide range of examples while most features are critical in only a few examples being used to fine tune the decision structure for special cases
given NUM a log file with the correct parse action sequence of training sentences as acquired under supervision and NUM a set of features the system revisits the training sentences and computes values for all features at each parse step
under tangible object abstract process verb or adjective with more depth used only in concept areas more relevant for making parse and translation decisions such as temporal spatial and animate concepts
several ways to estimate lexical probabilities were discussed and a new paradigm the genotype was presented
labels with no colon indicate the same input and output symbols
in addition no generalizations are made about similar context phonemes
we tested our induction algorithm using a synthetic corpus of NUM NUM input output pairs
an implementation of the algorithm successfully learns a number of english postlexical rules
table NUM agreement of annotations among speakers
null we broke the corpus texts into expressions using a simple sentence breaking algorithm and then collected the negative imperatives by probing for expressions that contain the grammatical forms we were interested in e.g. expressions containing phrases such as do n t and take care
this pruning was done when the example was not an imperative e.g. if you do n t see the mail tool window and when the example was not negative e.g. make sure to lock the bit tightly in the collar
in this discussion s refers to the instructor speaker writer who is referred to with feminine pronouns and h to the agent hearer reader referred to with masculine t ronouns dont imperatives
a dont imperative is used when s expects h to be aware of a certain choice point but to be likely to choose the wrong alternative among many possibly infinite ones as in NUM dust mop or vacuum your parquet floor as you would carpeting
if p a is the prot ortion of times the coders agree and p e is the t rot ortion of times that coders are expected to agree by chance k is computed as follows
NUM a pat will meet chris today
both tr2 and tr3 perform better than tri
if this ratio is greater than unity then the left branching analysis is chosen
i use this definition of focus as well
then a human instructor judges whether the clues are correct
osv this ace too father 2s quest give past
a japanese case class frame can be represented as a feature structure
bu defteri de gok say lira ben
first we define a noun partition pa
third the definition of e command is specified lbr this new obliqueness brmat
and NUM a whose grammatical status is not correctly predicted
okelo asked alaba about self okelo i asked alabaj about himselfi j
NUM zhangsani cong lisij chu tingshuo wangwu k bu xihuan zijii j k
the maria talked with the pedro about of the new director mary talked to pedro about the new director
therefore we would like to have an empirical basis to ascertain whether x does not o command y in this case
we adol ted the second method and used as a mort henm lexicon the set of hash keys representing the pos envirouments
for i oth experiments we considered tire five poss to which almost all unknown words in japanese belong i verbal noun e.g.
we call this value the word measure and accept as words all strings with word measure less than a certain threshold
the decision variables are the elements of tile probability distribution vector p which expresses tile likelihood that the string is used as each pos
for example in table NUM et nature is both a noun and the stem of a na type adjective
in practice instead of a single character we used as contexts the preceding or following postagged string a morpheme or word
since there are very few unknown words which consist of only one character this limitation will not have much effect on the recall
precision was calculated using the estimated frequency f pos p posl f tx
succ event i event i NUM
obviously there are certain dangers with this view
in this paper we sketch a decidable inference based procedure for lexical disambiguation which operates on semantic representations of discourse and conceptual knowledge in contrast to other approaches which use a classical logic for the disambiguating inferences and run into decidability problems we argue on the basis of empirical evidence that the underlying iifference mechanism has to be essentially incomplete in order to be cognitively adequate
again we have to choose which element to elaborate
in order to answer these questions let me take an example
from this prefl rrcd re ting we conchuh that the telescope is used as a levi to sec a man
finally step NUM of the algorithm ensures that a seed is created
magic compilation does not limit the information that can be used for filtering
figure NUM abstract unfolding tree representing the
a number of lexical entries have been added to the example grammar
magic predicates which produce variable bindings for filtering when evaluated bottom up
the modified versions of the original rules in the grammar are adapted accordingly
figure NUM simple head recursive grammar
magic for filter optimization in dynamic bottom up processing
most approa hes till now tl al ed subst rings of collo at ions as eollocal ions only if they apl eared ffequenl ly enough NUM y l hemselves in the cor lls
more hmi NUM cas frame examph s appeared in lira lraining data
note that this assumption is tile simplest among those that relax the independence a ssumption
from fl and we can conclude ii which is not ambiguous
the reason is that that f structures do not represent partial subordination constraints in other words they are fully underspecified
completed completed completed completed completed onp det n onp det n 2vp vt np 3np det n 4np det n
to obtain the prefix probability in c we need to sum the probabilities of all complete derivations that generate x as a prefix
note that the definition of path length is somewhat counterintuitive but is motivated by the fact that only scanned states correspond directly to input symbols
the spontaneous dot shifting described in the previous sections effectively performs the same operation on the fly as the rules are used in prediction and completion
NUM note that for scanned states is always a probability since by definition a scanned state can occur only once along a path
e step compute expectations for how often each grammar rule is used given the corpus d and the current grammar parameters rule probabilities
otherwise if ends in a terminal a let a a and call this procedure recursively to obtain the parse tree
this is modified to include a list of constraints which for the present purposes is presumed to be just duplication checks
psycholinguistie evidence suggests cross serial orderings tend to be easier to process t lmn nested cons ruc iions
in any case the metagrammatical method for parsing ww costs no more than just parsing strings in the characteristic language class of w
we show that for any language with up to context fl ee expressive power processing cross scrim dependencies can be accommodated without atfect ing tmrsing complexil y
neither need ww n for ps2 but the reversal language is a canonical example of a language that makes maximal use of the stack in the pda
all would like to thank catherine collin toma erjavec tsutomu fujinami merce prat fred p0powich mark steedman and the anonymous reviewers
the model of comput ttion here is an rpda in which only me spe cial symbol is allowed on the stack at any one time
for an ambiguous cfg there is no guarantee that multiple instances of a nontcrminal will rewrite to through the same sequence of productions to yield the same string
sayitch present s a chara ct erization of the languages ill te rms of stxingsets and the requisil e compu lal ional structures
our assumption is that what enables us to make a good prediction of barked is the syntactic structure in the past
the probability p w t can be broken into NUM NUM p
the model its probabilistic parametrization and a set of experiments meant to evaluate its predictive power are presented
figure NUM shows a word parse k prefix h NUM h lcb m rcb are the exposed headwords
null in the regular expression calculus the replacement operator is similar to crossproduct in that a replacement expression describes a relation between two simple regular languages
we show how a lexicon of french verbs ending in it inflected in the present tense subjunctive mood can be derived from a lexicon containing the corresponding present indicative forms
NUM the relation eliminates from the lower side language all brackets that appear on the upper side
n to l i when occuring between a left and a right context li and ri then every li and ri would map substring adjacent to ui
l he constraint relates instances of right brackets i and of right contexts ri attd is the mirror image of step NUM
make the constraints ai for every i and li and then intersect them in order to get tim constraint for all left brackets and contexts
i he term tc m expression NUM abbrevmtes a relation that maps any bracketed upper to the corresponding bracketed i ower
and a bracketed non empty upper i ui i is excluded from the corresponding a i NUM n by
in a codescriptive grammar semantic descriptions are expressed by additional constraints
if this fails it is reported back to the syn parser
when people engage in a conversation there are a number of cognitively intensive tasks that they have to perform other than encording and decoding internal structure of each utterance such as planning a larger unit of discourse planning and interpreting perlocutionary effects and paying attention to the surroundings
l ieco of informa t iou yore a coml ox clusl or ro luiros idetttit ying and liltdegring out irrolo wml itffot ma tion
semnet is shown to have the specified properties thus distinguishing it in tc rms of eili iency ms a suitable representation for large scale ni e
these eases need to be distinguished l or example con sider the three concepts the morning star the evening star and venns
it is important to emphasise that the information which is recorded within semnet is intended to reflect the world as it is to be understood by the agent that uses the network
existential e refers to the instances of the concept but the instance involved depends on the particular instance of some other universally quantified concept which is involved in the event
a status control makes this distinction it takes two va lues real when i oi i i a believes in the event and hypothetical otherwise
indeed one of the practical advantages of distributedness is that it does away with the need of inheriting all a nodes ancestors inr rlnalion while allowing the benefits of a hierarchieal knowledge base
if ro ading froth any node and in m y litdegorion givos iuformation which is sound wilh rospeo t o i h0 model
the olfioicncy ol lhis step dcl onds how iiflbrma ion is ordore NUM within ca oh h st er
l his pa l e r descril es multi moda l mel hod a inelho l for uil ling gl itilillia3 l ased
ilowever solile r el l systellls lla ve golie beyolid ot jeci s for lea ling with inierfa ce develop luenl
desiguers iiiay l of low hi developing niuil i lliodaj sys e iiis and provides mm i g a gl a iiillia ti a
a certa in leaf is missing in one of the trees but exists in the other one then we assign a ost s for this a structural dili erence
t note that i is possible to obtain more spa c reduction by ajso sharing any common postflxes of lhe vertex labe sequences using a directed acy lic graph representation and not a
it certainly is also possible that additional space savings can be achieved if directed acyclic graphs can be used to represent the tree database taking into account both comlnon prefixes and common suffixes of vertex list sequences
in this paper we consider the problem of searching in a database of trees all trees that are close to a given query tree where closeness is defined in terms of an error metric
from i heso synthetic i nb dm ses we ra ndo nly oxtra ctod NUM trees arid the perturbed thcnl with ramlom leaf deletions insertions and la bol changes so that l ioy were o some listmlce l ron t rees tree in the originaj tree
mat ehing standard searching with a trie corresponds to traversing a path starting t rcb om the start node o the trie to one of tlle lea nodes of the trie so that the concatenation of the labels on the arcs along this path matches the input vertex list sequence
for error tolerant matching one needs to lind all paths from the start node lo one of the final nodes so lhat wh en lhe labels on the edges along a path are concatenated lhc resulting verlea list sequence is within a given dislance lh rcshold t of the query vertex list sequence
given two vertex list sequences x and y the distance disffx m y n computed according to the recurrence below gives the minimum number of leaf insertions deletions or lea label hai ges necessary to change one tree to the other
the definition we propose here is a generalization of the canonical one to trees of any degree
note that chain i is non empty only in case rule i is such
the algorithm presented here might then be a good compromise between fast parsing and reasonable space requirements
we emphasize that r might be several hundreds large if the learned transformations are lexicalized
hence the total number of dead pairs must be o pt t
the other three classes were derived manually from a combination of roget s and wordnet since the relevant taxonomies were not available
by pre computing mappings and their statistics they implemented a considerably more implicit form of pba there is no explicit matching of the input string with lexical entries
best results of NUM NUM and NUM NUM words correct are obtained lor the pseudowords and lexical words respectively casting doubt on certain previous reported performance figures in the literature
by contrast tave is considered to be an exception pseudoword since it has the exception word have hay as an orthographic neighbor
seinowski and rosenherg NUM in which the generalised knowledge is learned e.g. by backpropagation its it set of weights and the network has no holistic notion of the concept word
in these tests the transcription standard employed by the compilers of the dictionary becomes its own ref erence and problems of transcription inconsistencies between input strings and lexical entries are avoided
the primary ability of a text to speech system must be to produce correct pronunciations lbr lexical words rather than pseudowords which just happen to be absent from the system s dictionary
here taze is considered as a regular pseudoword since all its orthographic neighbors raze gaze maze etc have the regular vowel pronunciation el
this is enormously poorer than their approximately NUM words correct yet the implementation reference pronunciations and test set are as far as we can tell identical
in this paper we seek to understand how dedina and nusbaum s largely unjustified implementational choices affected their results and thereby to resolve the conflict between their performance claims and sullivan and damper s
adjoin a leaf node labeled a as the right most child to the root of t and return t
in section NUM our approach to t he probl0an NUM he algorithm and an examl le are given
k a lal NUM n a n b NUM
for each of them the fact that they have been already l oun t in a longer string is kept as well
if we considered only the number of times il appears by itself it would get a low value as a candidate collocation
since we work with sublanguages we can use small corpora as opposed as if we were working with a general language corpus
assuine that the first string being a collocation is extracted by some method able to extract collocations of length two or more
regar ling its length we onsider hmger collocations to t e more important than shorter appearii g with the same fi equency
assume that the substring is a candidate ollocation if it appears by itself with a relatively high frequency
a pta usib e assumption on i he dependencies between random variables is intuitively that each variable direetbj depends oil at most one other variable
exploiting the stratified nature of the ebl specialized grammar we group the chunked rules by level and apply them one level at a time starting at the bottom
in the last ten years there have been a number of attempts to find ways to automatically adapt a general grammar and or parser to the sub language defined by a suitable training corpus
before both the phrasal and full parsing stages the constituent table henceforth the chart is pruned to remove edges that are relatively unlikely to contribute to correct analyses
at one limit the whole parse tree for each training example is turned into a single rule resulting in a specialized grammar all of whose derivations are completely flat
all possible grammatical analyses of each utterance were recorded and an interactive tool was used to allow a human judge to identify the correct and incorrect readings of each utterance
in the present paper the statistical tagging idea is generalized to a method called constituent pruning this acts on local analyses of phrases normally larger than single word units
taking minima means that the pruning of an edge results from it scoring poorly on one criterion regardless of other possibly good scores assigned to it by other criteria
we show how a general grammar may be automatically adapted for fast parsing of utterances from a specific domain by means of constituent pruning and grammar specialization based on explanation based learning
these methods together give an order of magnitude increase in speed and the coverage loss entailed by grammar specialization is reduced to approximately half that reported in previous work
experiments described here suggest that the loss of coverage has been reduced to the point where it no longer causes significant performance degradation in the context of a real application
it is also clear that relatiw ly few of these ra ndom variahles case slots are actually depeitdent on each other with any signiticance
now they do n t have vpe to
this happy bulletin vp convulsed mr gorboduc
we have added a label vpe for vpe occurrences
department of computing science villanova university villanova pa NUM
figure NUM parse tree for she said she would not
however these involved hand tested algorithms on rather small data sets
currently the system only examines the form of the auxiliary
exact match the system choice and coder choice match word for word
notions of parallelism figure prominently in many theoretical studies of ellipsis
this allows for the definition of cascaded as well as interleaved flow of control
the two constraints are maintained by the basic edges add nec and add opt
for example the recognition part for simple nominal phrases might be defined as follows
they are applied to their left and right stream of tokens of recognized fragments
a very efficient and robust german morphological component which performs morphological infection and compound processing
complex verb constructions in our current system fcps are attached to main verb entries
none of them make use of such sophisticated components as we do in smes
after a first trial run we obtained an unsatisfactory recognition rate of about NUM
however in future work we will investigate the integration of shallow linear precedence constraints
this constraint is nsefnl both in parsing sentences with extrapositions and in deciding where to use extraposition during generation
structural complexity measures how easy or di icnlt it is to establish these dependency links
vc also used lhe acquir d depend racy knowlc lcb gc ill a pl at l achmenl disambiguai ion
although the two adw rt i31 phra ses m c two different stri gs
furthermore in our approach no horizontal relations exist as the lexicon contains only one entry and no other entry is ever generated
roughly speaking the less specific the contextual information is the more inference power and expressivity is needed to retain the underspecification approach
without harming generality assume that the bivalent version of to load is in the lexicon and two lexical rules generate the trivalent versions
the exact treatment of such phenomena however goes beyond the scope of our discussion here which concentrates on the use of underspecification
this pointer is included as an extra feature of the value of synsf ivi loc cont nucleus
we used these data as input to the learning algorithm and acquired case frame patterns for each of the NUM verbs
it appears more natural to consider that the lexical realisation performed by rule r2 relies primarily on a correspondence between the predicate p and the predicative noun
meteer proposes to express the input semantic content in terms of abstract linguistic resources NUM 4f effectuer un nettoyage soignd du corps du filtre
we have found many pairs of bilingual instructions where the french instruction is based on an operator verb construction and the english instruction on a simple verb
the verb unlock is more specific than remove but the locking device to be removed is not specified as a surface argument of the verb
to illustrate this point let us compare the following surface realisations of the same instruction NUM e remove lockwire from filler bowl
however even when both instructions are at tile same specificity level differences may appear in the way semantic content is spread over the lexical material
such argument incorporation is often realized through the use denominal verbs which are much more frequent in english procedures ioe jack up the aircraft
by applying the recursivc analogical transfer process from larger linguistic constitucnts to subconstituents the system can handle various degree of lexicalization in the input language in an efficient manner
a plausible motivation of this preference is that as illustrated by example NUM s they impose precise selectional restrictions on the arguments
it should be stressed that in general both instructions are at the same specificity level even though one of them appears more complex
spoken language systems are thus required to transfer pragmatic utterance strategies at a more abstract level and to be able to recognize and generate appropriate surface forms in each language in order to achieve high quallty translation
some interjections and hedges can be used to help the listener prepare herself for the subsequent information or aid the listener s processing and comprehension of the current utterance
subovdinale w returns the subordinate words of tim word w
utomatic construction of a thesaurus and word sense d isa mbiguation
however existing thesauruses only represent unif lrm relationships between words
figure NUM shows the t o NUM categories of isamap
hit with rcb laniiner accompanier e.g.
ijrom a reas scattered throughout isamap
sets of con imcth l s
null l hird the structure of thesauruses is subjec hive
the depth and lensity of nodes it tree llke
in principle the assumption that only the terminals in unknown subtrees may be unknown can be abolished but this would lead to a space of possible subtrees which is computationally intractable
unfortunately the atis corpus contains several abbreviations and proper nouns that are not found in a dictionary and which therefore still need to be treated as unknown by means of dop3
with context a na lysis interfa ce
NUM mode interpretation should handle temporal inforlnation
m ulti modm meijiod defhies the procediire which hlllerfa ce
here while pointing a t a specific point
infor m t ion in va rying ways
NUM even though our dialogue model was originally not designed for language we have shown that this relatively NUM simple model provides useful information for intonation selection
figure NUM the drug experiment the similarity between a narcotic sense example to each of the two
pr wi i w log pr w NUM
we assume that different senses of a word correspond to different entries in its dictionary definition
figure NUM the drug experiment example runs sorted by the second iteration similarity values
note that the similarity is contextual and is highly dependent on the polysemous target word
might be only one of a number of complementary evidences of the plausibility ol a certain word sense
the algorilhm io disambiguate a given noun w in tile middle of a window o1 nouns w c f
the coverage in wordnet of senses lot open class words in semcor reaches NUM according to the authors
while local nhyp is the actual average for a given concept global nhyp gives only an estimation
the test set was tagged by hand allowing more than one correct senses for a single word
when conceptual distance is not able to choose a single sense the algorithm chooses one at random
coverage tends to get stabilised near NUM getting little improvement for window sizes bigger than NUM
the weights assigned to each clause are numerically equivalent to the number of transitivity features associated with each clause
NUM NUM c alcuh tc the description length for the two models before and after the mow as l1 and le respectively
which sl ands for using l hc learm d thesaurus altd vord net first t heu t he lexical associal iotl
model to be estimated is very large and therefore such a model is difficult to estimate with a reasonable data size that is available in practice
we can see easily that mdl genet al zes mle in that it also takes into account the complexity of the model itself
in particular we haw compared l h p u forn a nc0 of employing an m1 l based simula ted
all of these effects fall out naturally as a corollary of the imperatiw of best possible estimation the original motivation behind the mdl principle
in our simulated annealing algorithm we could alternatively employ the maxinmm likelihood estimator mle as criterion for the best probabilistic model instead of mdl
we could ill principle calculate the description length for each model and select a model with the nfininmm description length if colnputation time were of no concern
our experimental results indicate that combining an automatically constructed thesaurus and a hand made thesaurus widens the coverage NUM of our disambiguation method while maintaining high accuracy e
b transferable actions can be contrasted with nontransferable states e.g. he pushed her he thought about her
there exists a classical way to eschew the question what can be coordinated if one assumes a deletion analysis
more precisely we account for cases of non constituent coordination NUM of right node raising NUM but not for cases of gapping
these approaches have tried to account for coordination of different categories in reducing the constraint from requiring the same category for conjuncts to a weaker constraint of category compatibility
so we claim that we have nothing else to do but explicitly enumerate within the head subcategorization feature all the structures allowed as complement
technically the compatibility is checked by computing a generalization of categories and imposing the generalization comprises all features expected in the given context
the shaded nodes indicate where there are alternative parses
documents were obtained from three sources the internet optically scanned hardcopy occasional documents restaurant take out menus flmdraising letters utility bills and purchase from commercial or academic vendors
expected parsing error rate worked out to NUM NUM for the first three but NUM NUM for the other two treebankers while expected tagging error rates were NUM NUM and NUM NUM respectively
sample values for these attributes are friendly dense literary echnical how to guide and american south respectively
higltll ii ing of words and phrases is recorded along with the w riety of highlighting italics boldface large font e c
are performed by hand according to predetermined policies
in general and as one might expect the documents we have used were written in the early to mid 1990s in the united states in standard american english
this is necessary because the conceptual structures cs on which the rules are performed could be incomplete because of other types of ambiguities not being resolved
for example if the cf of the sentence is a pp object that is not attached yet in the cs the thematic rules fail to fill the cf
for example in sentence NUM the intrasentential antecedent bill will be taken into account because eel would be processed beforehand by the expected focusing algorithm
given that one template at the semantic level represents an elementary event the splitting is implicitly already done when these templates are created in the triggering phase
events an embedded sentence contains either more than one verb or a verb and derivations of other verbs see sentence NUM with verbs said and forming
intrasentential antecedents first of all we will distinguish the possessive reciprocal and reflexive pronouns prr hereafter from the other pronouns non prr hereafter
mainly we exploited sidner s focusing mechanism refining the classical unit of processing that is the sentence to that of the elementary event
we demonstrated that anaphors with intrasentential antecedents are closely related to embedded sentences and we showed how to exploit this data to design the anaphora resolution methodology
in case of a program failure resulting in the inability of a component to detach the 1ls is capable of handling the detachment autonomously
data size we had was not large enough
tree defines a different noun paxt ition
similar arguments apply in a host of other important applications such as text compression and language modeling for speech recognition where it is desirable for word and ngram probabilities to adapt appropriately to frequency changes due to various hidden dependencies
this problem setting is based on the intuit lye
we now turn to the question of what
we empiricmly evmuated the effectiveness of our method
ma ny a ctuaj multi modaj system a rchitectures are combina tions of these extrelne a rchitectures
knowing the first gives no information about the third
the rules that occur more than one time are
figure NUM output of ot fst system a ooy k zozoooooooo2ooooo2 moqmen s ucut i ww p pwwwww l syllable stmctu 00nonconc phonen struclm g r u m a d w e c figure NUM pruned output of ot fst system
in the one level version of ot that most computational methods use the space is populated with candidate outputs i some of the computational work in ot confusingly uses the term parsing to refer to generation
it is not obvious though how the input can be found from the search space in ot
we have proposed using two level ot to extend e1lison s technique for representing constraints as finite state transducers
he is able to build fst representations for the constraints that he considers showing them to be regular
with two level ot the mapping from input to output can be directly operated upon by computational theories
in ot a system of constraints selects an optimal surface output from a set of candidates
the system has been fully implemented in common lisp and c
in all of the three applications we obtained high coverage and good results
morphological processing follows text scanning and performs inflection and processing of compounds
the output of mona is the word form together with all its readings
in germany ie based on innovative language technology is still a novelty
in this way agreement information is propagated through the whole fst
thus seek can also be seen as a macro expanding operator
the knowledge base is the collection of different knowledge sources viz
the filtering rules are also used for tagging unknown words
the corpus consists of a set of monthly reports jan
lexical heads phrase structure and the induction of grammar
i also thank three anonymous reviewers for their comments
the latter approach has become increasingly popular e.g.
dop1 uses substitution for the combination of subtrees
for the adjustment of the frequencies of unseen types where r NUM r is equal to n1 no where no is the number of types that we have not seen
thus nr is the frequency of frequency r
NUM how does dop perform on unedited data
NUM NUM the model dop2 the partial parse method in order to get a feeling of the problems emerging from word parsing we propose as a very first tentative solution the model dop2
as to the parsing of word strings we show that the hardness of the problem does not so much depend on unknown words but on previously unseen lexical categories of known words
the result of a mismatch between a subtree and one or more unknown category words is a subtree in which the terminals are replaced by the words with which it mismatched
thus if there is an unknown subtree in the test set then there is a subtree in the training set which differs from the unknown subtree only in some of its terminals
specifically if x has a production
NUM b NUM NUM linking and bottom up filtering
andreas stolcke efficient probabilistic context free parsing
the new probabilities can be computed as
this would leave the forward probabilities at
via any number of intermediate states
the third experiment is performed on two of the test discourses in the next section by implementing two versions of the centering algorithms that are mentioned in the hast section anti wn ying tim range of simple sentences where the
secondly in the centering theory it is unclear whether two zero pronouns with the same grammatical property in the different simple sentences of a complex sentence can be simultaneously handled without any extension to the original theory
with the centering theory in the centering theory that we outlined in the last section sentence that is its basic unit of processing means the simple sentence that contains only one predicate verb
we take advantage of the simple heuristic the more arguments a verb has the more specific the resulting instruction in order to detect potential conflicts
it should also be mentioned that such basic correspondences can also be exploited to generate similar phrases in other types of constructions
in order to measure the closeness between an unclassified document and the NUM domains we proposed a feature space the axes of which are domain specific kanji characters extracted from the NUM domains
however it is difficult to extract these significant words from japanese text because japanese texts are written without using blank spaces such as delimiters and must be segmented into words
characters by the x NUM method using the value x NUM of the x NUM test we can detect the unevenly distributed kanji characters and extract these kanji characters as domain specific kanji characters
considering that our system uses only the statistical information of kanji characters and deals with a great amount of documents which cover various specialties our approach achieved a good result in document classification
for extracting doiriain specific kanji characters and obtaining the fea till e voctoi s of each domain we ilse articles of l ncych rcb pedia lh ibonsha
from this we have extracted the fixed number of domain specific kanji characters from every domain using the ranking of the value x d of equation NUM instead of NUM
for example physics electric engineering and german literature are the code domains of the ndc and we know these domains are not well balanced by intuition
the lexicon maintains four tapes pattern root vocalism and affix tapes
moreover in the case of japanese texts it is difficult to extract words from them because they are written without using blank spaces as delimiters and must be segmented into words
figure NUM variatious of the classification results for
we outline the problem solver using a sample problem of how to move from the musashino center to the atsugi center on the map in figure NUM
even when the solution has been partially determined this model starts utterances to satisfy time constraints where pauses in mid utterance must not exceed a certain length
the word menace which is a hint for the narcotic sense in this sentence did not help in the first iteration because it did not appear in the narcotic feedback set at all
the relatively small number of polysemous words we studied was dictated by the size and nature of the corpus we are currently testing additional words using texts from the british national corpus
we presented the results of an anmysis of discourse structure and showed that speakers frequently use small information units and exploit the fine structure of discourse that contributes to the increlnentm production strategy
as can be seen wk wk ltk NUM t k k ti l is one of the nk word parse k prefixes of wktk i NUM nk at position k in the sentence
however using such hand crafted categories usually leads to a coverage problem for specific domains or for domains other than the one for which the list of categories has been prepared
thus in the medicine cluster there one finds words such as analyst campaign profit quarter dollar which serve as hints for the medicine sense
note that after several iterations the similarity values are close to NUM and because they are bounded by NUM they can not change significantly with further iterations
this kind of similarity therefore suits its purpose which is sense disambiguation although it may run counter to some of our intuitions regarding general semantic similarity
the method we propose circumvents this problem by automatically tagging the training set examples for w using other examples that do not contain w but do contain related words extracted from its dictionary definition
the tbnner are extracted by dictionary look up mid morphological analysis
figure NUM an overview of the representation used by the model
vbd is identified as the head child of vp vbd np np
v case n NUM contributes n NUM dependencies
loading the hash table of bigram counts into memory takes approximately NUM minutes
the method is equally applicable to tree or dependency representations of syntactic structures
it is thus necessary to determine in both cases which nod will be in charge of introducing the discourse referent
the combinatorial method used here is a conversion with two kinds of expressions namely predicative drss and partial drss
and since these new kinds are defined in terms of others kinds of discourse referents this may well be a non trivial task
the aim of this study is to see how negation interferes with so called discourse relations continuation elaboration explanation
in constrast the second sentence of 5b introduces a state which overlaps with the event of the previous sentence
finally we have t take into account the possible role of temporal a tverbials which predicate over t
giw s negation a wide scope over events or states and that the syntactic results correspond with the semantic ones
sccll s the ontological and semantic i rot erties of such special vents remain to e defined and tlw
jill radio frequency heating s simple word c compound word and NUM NUM respectively and those after feedback were NUM NUM and NUM NUM
NUM use of symbol numeral correspondences in the present implementation the correspondences of symbols and numerals are not used in calculating the correlation because the bilingual dictionary does not contain them
first japanese co occurrence set c jw is transformed into pseudo co occurrence set cl jw by consulting bilingual dictionary d which is a set f pairs of words
NUM use of the constituent word information of compound words the key idea of our method is to associate a pair of words through their co occurrence information with the assistance of a bilingual dictionary
the point is that even if the pair of words to be associated is missing in the bilingual dictionary their co occurrence sets can be associated through the bilingual dictionary
the bilingual processing then calculates the pairwise correlations between the co occurrence sets for japanese words and those for english words and selects the pairs of words with the highest correlations
we use the set of words co nccurring with word w which we refer to as the co occurrence set of w to concisely represent tire accumtdated contexts characterizing the word
correlation the absolute values of the correlations are not significant because they are sensitive to the numbers of words in the co occurrence sets which vary considerably from word to word
accordingly we evaluated two cases case a the already known pairs of words are included and case b the already known pairs of words are excluded
the pseudo recall indicates the lowest limit of the recall since a word in the japanese text does not always have a straightforward counterpart in the english text and vice versa
similarly while both versions of to load are related to passive adjectives loaded carl loaded hay only the location version is related to such an adjective in the case of to stuff stuffed pillow stuffed feathers
this feat ire we ttalile sfm antic cons fii ainq s a tl we make it apl rol riate tbr the same values that the prepositional synsemii oc cont nuci eus is assigned
while this intuition is clear this is much less adequate an approach for morphological relatedness where a componential approach may often appear just as natural as an object relatedness view especially if the formalism includes fimctionally dependent values permitting the expression of allomorphic wtriation and the like
horizontal relations must be constrained to account for exceptional behavior that is for those words which do not participate to a given horizontal relation despite the fact that their description makes them appropriate candidares for the relation verb alternations offer several examples of these situtation for instance giving verbs which do not exhibit the so called dative shift phenomenon
rccoveralfle NUM nodos are labeled with atil ratnnlar rule nantes ratl t t ha as is lll l e tlsllal with lloltl rlttillal
an estimate based on the identities of the two tokens alone is problematic
note that the formula mnnbered NUM contributes to both of tile sucessflfl overall mtalyses without needing to be recomtinted
eliminations and introductions correspond to steps of functional application and abstraction respectively as the lambda term labeling reveals
consequently tile o i rule is not required and hypothetical reasoning excluded
the alternative regime is easily achieved by making the condition c c
other NUM ositi ns will NUM e tilled with anonymous varim les
l ly storing su h vet tots with formulae in the datalm se
resull s wheal qcarching exhausi ively for ll possible mmlyses
a precise statement of the compilation procedure r is given in figure
a minor complication arises for using this approach with the compilation chart method described above
figure NUM shows how constituents in the chart combine in a bottom up manner
ii definition NUM angiophtsty of a coronary artery NUM inclusion angiol lasty angiol lasty purlmr tedx l j ar t n y eg t pal t coronary artery
in the context of a predicate one of the concepts in the reference model is selected as the incolning point of a link from the predicate s inealfing representatk n the coneel t oriente d dommmnlodel apl roaeh advocated here hyi othesizes that the behavior of words is driven by their conceptum ro es in the domain
in the context of our application angioplasty acts on an artery segment a physical object corresponding to a part of an artery which happens not to be comparable to any of the four preceding themes of angioplasty NUM therefore all four examples NUM NUM must be considered as metonymies
we address here the treatment of me tonymie expressions from a knowledge representation perspe tive that is in the context of a text understanding system whi h aims to build a onceptual representation from texts according to a domain mode l ext resse d in a knowledge representation formalism
we implement this by heuristic graph traversal through the reference models and the type hierarchy looking for a chain made of concepts and conceptual relations i.e. a linear conceptual graph which could link concepts of the same types as c1 and c2 and at the same time would satisfy the conceptual preferences of gr
we explain how we use tile domain model to handle metonymy dynamically and more generally to un lerlie semantic omposition using tile knowledge descriptions atta hed to ea h oneept of our olttology as a kind of eon el t h ve l
the semantic definition of t word is here the reference model of its head concept type each relation path starting fi om the head eon ept of this reference model is similar to a qualia role in that it describes one of the semantic facets or NUM ossible uses of the word
the next one is the reference model associated with the type t of the head concept of the definition
the models that associate knowledge to a given predicate p can be ranked according to their level of generality
NUM prefer the shortest derivation sequence for each input substring
linked nonterminal must be derived from a sequence of synchronized pairs
link constraints are propagated from source to target derivation trees
one more variable allows us to express r in terms of a and a let a be the probability that an arbitrary co occuring pair of word tokens will be linked regardless of whether they are mutual translations
itt st al istical na t ttral languag i rocessing it
the arrow connecting vk and u l in figure NUM represents an indirect association since the association between vk and uk z arises only by virtue of the association between each of them and uk
however n u v n u v which is not the same as the frequency of u because each token of u can co occur with several differentv s
to find the most probable values of the hidden model parameters a and a we adopt the standard method of maximum likelihood estimation and find the values that maximize the probability of the link frequency distributions
to n o unl and otherwise including when both are 1degeach cut in a t hesa urus
the coding system of bgh has a hierarchical structure that is the first digit represents the part s of speech of the word NUM noun NUM verb NUM adjective NUM others and the second digit classifies words sharing the same first digit and so on
limited vocabulary size unclear classification criteria building thesauri by hand requires considerable time and effort the vocabulary size of typical handcrafted thesauri ranges from NUM NUM to NUM NUM words including general words in broad domains
ovstra int in such a nlanner that NUM olysemous words with the slune lemma within a context ha ve th same nt of he is r tle q d in he translation of the predicate like
similarly tile information on the unaml iguous prepositional phrase in placed on an output queue in sentence NUM disaml iguates the aml iguous i rel osi tional t hrase on a job queue in sentence NUM alh wing it to be attached to places
the longest sentences in th e susanne corpus consisting of NUM words was parsed in NUM seconds
a single timing datum is shown for te and st tests because they are generated in a single run
if it is the sequence words will have the same semanti c features as the previously recognized entity
failing to recognize coke as a company name was partly responsible for NUM te errors
the words john dooner will succeed james were grouped together as a single person name
a pair of proper nouns in which neither is a substring of the other refer to different entities
dooner jr status unk the double rule is triggered by succeed in NUM
some evaluation results are presented as well
the author is a member of the institute for robotics and intelligent systems iris and wishes to acknowledge the support of the networks of centres of excellenc e program of the government of canada the natural sciences and engineering research council nserc and the participation of precarn associates inc
incorrectly treating fallon mcelligott as a person instead of a company caused NUM te errors
conversely sw t close to NUM NUM means that t is a strong negative indicator since it occurs nearly always with the rejected candidates
classify phrases in order to classify a candidate phrase all evidence items need to be collected from its coiltext and their sw weights are combined
the seeds that we used in our exlmrimenl s are quit simple perhaps too simple lletter seeds may be neede d
a naive spotter may contain simple contextual rules such as those mentioned above e.g. for organizations a noun phrases ending with co
group a items are collected from the candidate phrases that are accepted by tile spotter group r items come from the candidate phrases that are rejected
weights of evidence items within an evidence set are then combined to arrive at the compound context weight which is used to accept or reject candidate phrase
the poinl with the inaximmn precision recall in the ftmrth rllll is NUM lcb pre ision and NUM recall
in can also be applied to building other than the most common spotters such as those for people names place names or company names
surface desc obj o c it utter expressions to describe doinain object o as an object having content c and bearing thenmtic relation r to a certain event
let us consider the role that tile l redominant relations elaboration circumstance and motivation l lay in tile inereinental strategy of utterance t roduction
in the disconnect case for instance the two items connector and connectee are both integral elements of the situation
c0v additions to the covering list r0c role changes in psemspec nr0 additional psemspec roles and fillers
this choice of process and participants in effect establishes a perspective on the situation sitspec is underspecified in this respect
they made an additional distinction between obligatory and optional actants somers NUM ch
introducing a primitive though amounts to conceding that no explanation in terms that are already known can be given
now the alternation rule extends the denotation to also covering the event and the activity that brings the filling about
we have shown that the entire alternation space for a verb like to drain can be generated in this manner
in this graph every verb base form has an entry point corresponding to the aktionsart of its most basic configuration
the denotation of such verbs thus involves a pre state and a post state which is the negation of the former
now an extension rule has to systematically derive the causative form tom drained the water from the tank
for reasons of affordability their designers appear to have made no attempt to tackle the well known problems in mt such as how to ensure the learnability of correct translations and facilitate customization
incidentally the players will gain greater utility if they use the combination of angry fred max and he was angry with the man which is consistent with the optimal equilibrium of the np games
1segment in our ontology corresponds to a portion of space not of matter
when inultiple chains remain in competition one is selected randomly
NUM concept inclusion a concept may be included in the other s model
in the past research the distributional pattern of each case slot is learned independently NUM one may argue that fly has different word senses in these sentences and for each of these word senses there is no dependency between the case frames
the feaspar architecture consists of neural networks and a search
feaspar a feature structure parser learning to parse spoken language
this specification was already available as an interlingua specification document
to compensate for this we wrote a search algorithm
these vectors are used as input to the neural networks
a special atomic feature value is called lexical feature value
second we describe the parser architec ture and how it works
feaspar performed better than the lr parser in all six comparisons that are made
the neural networks first spilt the incoming sentence into chunks
the system have one parser and one generator per language
for example in the wall street journal the word medicine is mentioned mostly in contexts of making profit and in advertisements
analysls entries for in prepositions nay occur in strongly bound pps where they are functional elenrents semantically empty but syntactically relevant
it shows the functor treatment applied in the german grammar namely that the functor selects its head dtr combines itself with the head dtr and projects the mother
null the ps component covers all categories especially all clausal categories main clauses subordinate clauses relatives nps aps advps pps
the corpus based approach to grammar development is the only realistic way to get closer to a coverage that is interesting from all application point of view
input in den wochen vor weihnaehten konnte der stolze vorsitzende der zu daimler benz gehoerenden deutsche aerospace ag ein jahresergebnis das alle erwartungen uebertraf verkuenden
semantic entries for qrlh prepositions need semantically different entries depending on whether the p heads a pp which is a conlplentent or it adjunct
so e.g. the verbal suffix t has lots of interpretations 3rd pets sing 2nd pers pl preterite and more
mainstream approach the approach claims to be mainstream very much indebted to hpsg thus based on the currently most prominent and recent linguistic theory
coverage most grammar development projects are not based on an investigation of real texts but start from the linguists text book
NUM the past participle extensions here are purely for the sake of the formal example they have no role to play in the morphological description of english but cf
2rod of course we can not simply use iii as it is since it only applies to the particular word described by word1 namely loving
here we define default present morphology to be simply the root and this generalizes to all the longer forms except the present participle and the third person singular
NUM datr makes a distinction between a path not having a value i.e. being undefined and a path having the empty sequence as a value num
in this example num one has the value one rum two has the empty sequence as its value and iojg three is simply undefined
in this section we briefly discuss a number of technical issues relating both to datr as a formal language and also to practical aspects of datr in use
they are in fact surprisingly consistent in this regard as can be illustrated for the indicator nouns discussed in section NUM there it was noted that man is an indicator of the aged sense of old with antonym young and house for some of the not new senses of old with antonym new
similarly the correctness sense of right refers both to decisions and to decision making entities the latter primarily human among NUM different nouns modified by right in the co occurrence sentences NUM of the nouns modified by not wrong instances are human and all NUM nouns modified by not left instances are human
the sense of an ambiguous modified noun may be needed to determine the relevant semantic attribute for disambiguation of a target adjective and other adjectives verbs and grammatical constructions all show evidence of high reliability and sometimes of high applicability when they stand in specific well defined syntactic relations to the ambiguous adjective
a role noun almost always refers to an individual animate who stands in that relationship to another as in all the example sentences cited below for example when the nuns new and old filed out of the cloister it was a set of persons and not of relationships who did so
in the case of old it is a restricted version of the feature living thing for right of animate people and animals and for short human beings happen to be a frequent instantiation of a verticality feature that is normally appropriate to woody plants and to relatively large land animals
unlike the previous section it does not point to any automated procedure to take advantage of these properties or to the role they might play in some more encompassing procedure and it uses coverage and reliability as measures of the actual association of adjective senses with other constructs irrespective of their recoverability from raw text
others such as car involve issues of fashion and decoration to which color and thus darkness is potentially relevant as well although the NUM instances of light car s in our corpus do refer to weight with dark car s referring to a darkened interior
in addition to requiring that the target word functions as an adjective we exclude all freezes NUM as well as quantificational expressions such as three years old in which the target labels an attribute e.g. age rather than a value of that attribute e.g. aged
li urthermore equations are set up at the level at which there are needed e.g. at the vp level in the case of a pre verbal
in l ooth s approach the fsv is detined by re ursion on the truth conditional structure which is itself derived from lf i.e.
in particular it relies neither on the use of quantifier l lcb aising nor on the assumption of a rule to rule definition of the fsv
in other words the right adjacent sibling of a b us operator must contain all and only the loci this operator associates with
note that rooth s approach criticajly relies on quantifier raising as a means of moving a focused np out of the scope of a focus operator
in contrast quasi soes only involve semantic equivalence between repeating and repeated material for instance in a quasi soe a repeated element may be pronominalised
fig NUM supplies a simple exmnple of nkrl code
nkrl a knowledge representation language for narrative natural language processing
we can note that the enunciative situation can be both explicit or implicit
the use within file rules of clever mechanisms to deal with the variables
the nkrl code can be used according to two main modalities
ment al pertains to the modality modulators
this happens whenever there are few pairs of suffix trees of trees in lhs r that share a common prefix tree but no tree in the pair matches the other at the root node
suppose some i such that i NUM suppose we want a recognizer for lcb ww w e lcb a b rcb rcb where w e psi then we can use a parser that is no worse than cubic if i NUM and which can be linear if i NUM to determine if w eel
labeled l precision recall measures not only structural correctness but also the correctness of the syntactic label
it breaks parsing into an ordered sequence of small and manageable parse actions such as shift and reduce
before learning a decision structure for the first time the supervisor has to provide an initial set of features 4supported by acquisition tools word concept pairs are typically entered into the lexicon and the kb at the same time typically requiring less than a minute per word or group of closely related words
table NUM compares four different machine learning variants plain decision lists hierarchical decision lists plain decision trees and a hybrid structure namely a decision list of hierarchical decision trees as sketched in figure NUM the results show that extensions to the basic decision tree model can significantly improve learning results
boxes represent frames the central data structure for the parser consists of a parse stack and an input list
recall i labeled tagging tagging accuracy cr snt crossings per sentence ops correct operations opseq dian in january down NUM o from december s NUM NUM billion billion on a seasonally adjusted basis statistics canada a federal agency said
this definition is slightly different from that of the word class association score in that it only needs the set eg vj p for a japanese verb vy and a japanese case marker p but not the whole 3apanese english parallel corpora
then conditional probabilities NUM c iv j pr fa ira and i r cm fjiva are defined as the ratios of the numbers of the elements of those sets
since the classification process can be regarded as sorting the calculated association score its conlputational complexity can be o ieg vj i log ieg j l if efficient sorting algorithms such as quick sort are employed
then conditional probabilities pr ce ira p pr cj i va p and pr ce cj i vj p are defined as the ratios of the numbers of the elements of those sets
bgit has a six layered abstraction hierarchy and more than NUM NUM japanese words are assigned at the leaves and its nominal part contains about NUM NUM words NUM roget s l hesalrus has a sevenlayered abstraction hierarchy and over NUM NUM words are allocated at the leaves a
NUM for example if a japanese polysemous verb vg has both intransitive and transitive senses pairs with the subject case like era subj cjl cek s l bj
then the measure of the association of classes of english predicates and japanese case element nouns i.e. a measure of bilingual class class association is introduced and extended into a measure of bilingual class frame association section NUM
the column one sense jluster means that each cluster contains examples of only one hand classified sense and the sub eohmms ci and eg list the number of sllch lusters and the sum of examples contained in such clusters respectively
for example using some measure it is desirable to automatically discover the fact that for the task of sense classification of verbal polysenry subject nouns are usually nlost effective for intransitive verbs while object nouns are usually most effective for transitive verbs
we have attempted to account for the preceding results in a language assessment model called slalom steps of language acquisition in a layered organization model
so for example the figure indicates that while the s plural ending is being acquired NUM we intend this figure as an illustration only
that is the system should attempt to generate texts which use constructions that are at or slightly above the level he she is placed in slalom
as a result tutorial dialogue explaining appropriate verb morphology is quite appropriate and has a good chance of having a positive effect on the leamer s writing
in the second sample the ing form of the verb should appear affter the helping verb am but the bare form is included instead
for example the problem of identifying errors in the original input text may seem like an issue of straight analysis but it is not completely so
in this paper we present a portion of our work that describes the student s generation process as it is affected by second language acquisition sla
because of this vastly different method of realization we might expect and often do find problems in sentences involving a main verb of to be
presumably this model should capture the generation process of a learner who is using a model of their first language as a basis for generating in the second
the described situation may seem somehow artificial yet NUM believe it occurs quite often in spontaneous discourse even if we are not aware of it
let us assume that the interpreter compute a backtrack point
any alternative is based on backtracking at least one rule
a tgl rule is applicable if its preconditions are met
and are list delimiters denotes coreferences
order of nucleus and satellites in rst based analyses mann and thompson
this leaves us with two possible directions that can lead to improved results
these cases can be detected by maintaining a trace of all constraint actions
this is highly efficient for backtrack points introducing cheap alternatives e.g.
the post contexts for all backtrack points can be entered into the table
every tgl rule has a unique name denoted by the initial string
the crucial point is that gen hides the surfaceform to candidate mapping in ellison s system the eval portion of the system only combines with the output of gen so the mapping is lost
he requires that eval constraints output binary marks when ranking candidates and be describable as a regular language the output of gen must also be describable by a regular language
NUM harmonic comparisons between the candidates will consider the candidates with the smallest number of NUM marks first followed by the smallest number of NUM marks
we suggest consequently to order partially the arguments of every lexical subcategorization
back primed words are then popped out
each priming step involves two successive processes
it however leads to a noticeable increase of perplexity
the inputs represent the priming words
it characterizes the meaning dependencies inside the sentence
as interaction between autonomous agents communication can be analyzed in game theoretic terms
null the girl will fly a jet
r would do so even if r knew that it is not raining
table NUM verbs and their dependent slots
what resulted are class based case frame examples
the gm will fly japan amines
this amine company flies many jets
the aend action end value is a variable used as the placeholder of the action s ending time point
a precondition c of an action can be represented by a triple similar to a fact token
there are cases where state dependent interpretation is iml ossible unless the effects and preconditions of actions are treated
an unbound time point variable is taken to be greater than any integer and to be less than t
given an edge e and its field field field e denotes the value of field in e
not only syntactic adaptations to another feature formalism were needed but also the olmrational characteristics of fuf had to be accounted for
the next section describes the original version of autoslog as well as the new version autoslog ts that generates concept node dictionaries automatically using only a preclassified training corpus
it is impossible to look at an isolated noun phrase such as john smith and determine whether he is a perpetrator or a victim without considering local context
in this section we describe the original autoslog system for automated dictionary construction and then present autoslog ts a variant of autoslog that does not rely on text annotations
from a practical perspective it is important to consider the human factor and to try to minimize the amount of time and effort required to build a training corpus
NUM but perhaps even more importantly the annotation process is not always well defined in many cases it is not clear which portions of a text should be annotated
for example in the terrorism domain it is essential to have a pattern for the expression x was killed because people are frequently killed in terrorist attacks
o leftcontext c left oriented lcb vl l1 NUM NUM rcb NUM leftcontext o replace o rightcontext d downward oriented lcb NUM l1 NUM rl rcb NUM replace o leftcontext o rightcontext
w6 wgm qupsn zhi chi ta de yl jign
hong kong s stabilize boom is us life styles s pillar
our work differs from the id lp work in several important respects
sentences of length f with and without the btg restriction
these parameterizations were only intended to crudely model word order variation
offset alignment or distortion parameters are entirely eliminated
the improvement in speed does not appear to impair accuracy significantly
these arrangements can enforce us future kept financial stabilization s competency
right hand side symbols are visited left to right for chinese and right to left for english
in a sbtg a probability is associated with each production
finally entries themselves are extended with information common to all their derived variants
of course we could hypothesize that verb formation is preempted by synonymy e.g. by drive
many lexical rules involve changes in subcategorization and automatic techniques for extracting subcategorization from corpora e.g.
figure NUM lexeme for lacquer the probability of an unattested derived entry given a word form
thus even if the narrow class approach is correct its implementation is problematic
note that there are states with no associated probabilities reflecting possible but unattested usages
but it may undergo either the benefactive dative or recipient dative rules to yield a dative realization
this approach fits in most naturally with systems where probabilistic information is incorporated systematically
there is still the potential for false positives if an adjectival ed form e.g.
we looked at four classes of nouns vehicles dances hitting weapons e.g.
for example in the specific triggering f unily of nkrl rules the antecedent variables a variables are first declared in tile syntactic antecedent part of the rules and then echoed in tile consequent pro is where they appear under the form of arguments and constraints associated with the roles of the activated templates
for exmnple in fig NUM the list eonstr specialises the constraints on some of the variables while others e.g. the constraints on the v uiables xl humanbeing social body and x4 planning activity are unchanged with respect to the constraints permanently associated with the variables of template producfa
we reproduce now fig NUM one of the several triggering rules to which tile lexical entry call pertaining to tile nl fragment examined at the beginning of section NUM contains a pointer i.e. one of tile rules corresponding to the meaning to issue a call to convene
the candidate news items about NUM have then been translated into nkri formal and examined through a query system in order to i confirm their relevance ii exlract their main content elements actors circumstances locations dates amounts of shares or money etc
templates instances predicative occurrences i.e. the nkrl representation of single specific events like tomorrow i will move the wardrobe lucy was looking for a taxi peter lives in paris are in the domain of the factual component
the most interesting component of tile fum module is represented by the matching algorithm which unifies the complex structures like specif summoning l specif board meeting l mediobanca special in occurrence c2 of fig NUM that in the nkrl terminology are called structured arguments
moreover we assume that the three word probabilities in the case frame of an interpretation are mutually independent and define the geometric mean of the three word probabilities as the lexical likelihood of the interpretation
for example the two interpretations of the noun phrase shown in figure NUM a have an equal likelihood value if we employ pcfg although the former would be preferred according to rap
furthermore lex3 lex2 and syn formed NUM NUM NUM NUM and NUM NUM of the disambiguation results respectively
our main proposals are a to unify the psycholinguistic approach and the probabilistic approach specifically to implement psycholinguistic principles on the basis of probabilistic methodology
since in english the head word w of category h tends to locate near its left corner we can approximate d as l the number of words contained in h
we also selected the interpretation with phrases always attached to nearby phrases as the most preferred ones and randomly selected interpretations from what remain as the nth most preferred ones
syntactic preference of the attachment can then be defined as s ll i2 ik l r1 r2 rk
most psycholinguistic principles have been developed on the basis of a vast data base of actual observations and thus a method based on them is expected to achieve good disambiguation results
if we were to define a lexical likelihood as the product of the three word probabilities in the case frame of an interpretation an interpretation with fewer case slots would be preferred
consider again the following sentences he did not notice him
the decision trees describe the behavior of the machine at a given state in terms of the next input symbol by generalizing from the arcs leaving the state
a decision tree is induced for each phoneme classifying possible realizations of the phoneme in terms of contextual factors such as stress and the surrounding phonemes
zero pronoun resolution is perforlned by the original centering algorithm
for example the transducer of figure NUM will insert an ae after any b and delete any ae from the input
this suggests that the traditional formalism of context sensitive rewrite rules contains implicit generalizations about how phonological rules usually work that are not present in the transducer system
our first modification of ostia was to add the bias that as a default a phoneme is realized as itself or as a similar phone
with r al dis ours s
a tree transducer constructed by this process is shown in figure NUM for comparison with the unaligned version in figure NUM
b jiro ga koe wo kake temo kizukanakatta
a model of a given type has an identified head concept with the same type and the network of its related concepts represents its associated knowledge
no ewfluation has been performed on 1here basic components of the system we can however provide statistics drawn from the global test for the semantic analyser
the conceptual representation of an actual grammatical link will therefore be computed dynamically by the semantic analyser using its context the linked predicates and domain knowledge
this process is driven by a controlled natural language grammar which specifies possible expansions at each point of tile derivation
we will focus on how it supports the author in augmenting the information automatically acquired dora the interface design tool
the text planner selects the content appropriate for the instructions and builds a deep representation of the text to be generated
the presented text is mousesensitive allowing the author to access the knowledge base entry for selected part of tile text
the knowledge grapher prevents tile author from losing orientation by maintaining the current state of the procedural structure in graphical form
their help messages indicate the actions a user can perform in a partitular situation and what would result from these actions
please uote that i consider both man and the variable attribute as sister nodes hence in principle there could be a linearization problem
top down left to right expansion gives the speaker a good control over the whole process minimizing tile danger of forgetting some information because of memory overload
this suggests the corrolary questions is all information pertaining to a given module processed in one go one shot or am objects gradually refined several passes
the resulting conceptual chain as a whole represents both the specific facet of the argument which is involved in the sentence and the conceptual role it plays in the predicate
for instance if it is t he case that such a nmchanisin exists then patterns of string copy disthtency should ocellr with lifferenl
functions as well as predicates have to be introduced by signatures that define informational lower bounds on the arguments if present and the return value
NUM src set obj path rcb oh sac current position index
since the rule will be instantiated for each item represented in the underspecified feature structure the prices of all objects will be appended to the text variable
if the evaluation of one of these predicates fails the name of the failing predicate and the variable instantiations can be passed on to an error handling procedure
if the signature of the function or the predicate requires the argument to be defined all feature structures for which the path 7r is not defined are removed
the method is constrained enough not to augment overall processing complexity implying that ww does not require the worst case recognition complexity for its characteristic class the mcs languages
the following rule copies the index of the intersection of the current position into the path object if the source of the path is not specified
the forward chaining inference procedure allows the system to react information driven which means that in essence the information entered into the system determines which rules are evaluated
so in relation to the verb afelippen to cut off we find among others direct object and means
it distinguishes the following concept types cc surgical deed indicating the surgical intervention cc anatomy indicating anatomical concepts
the list does not present just a list of words unknown to the system but a selection of words relevant to cen
like for case grammar there are markers like prepositions or the lack of prepositions which point to a certain semantic link
the number of words that were assigned correct classes by both methods was NUM NUM which represents NUM of the words correctly classified by our method and NUM of the words correctly classified by nakano s method
for filling in the frame and analyzing the sentence the system performs the following steps for each surgicaldeed clause null ex
in most cases the element for which the concept type is guessed consists of more than one word ex
the efl cts of a linguistic action in a dialogue mainly i roducc unobservable mental state changes of the dimogne participants
hereafter we focus on bottomup active chart parsing although the core of the discussion below can be applied to other parsing methods
the initial state is treated by using a special initialize inactive edge from NUM to NUM with the effects value representing it
if the verb has at least one of the semantic links of the entry cs neutral it will be considered as a surgical deed concept ex
given an observed action chart parsing applies the following procedure procedure NUM let j be the description of the j th observed action
the influence of the qnitialize edge is propagated by the procedure for treating action enabling relationships and preference rules referring to precon litions a
for all non surgical deed concepts namely cc anatomy cc pathology cc combi and cc intervent equipment tim following frame has been defined
many multilingual nlp applications need to translate words between different languages but can not afford the computational expense of inducing or applying a full translation model
however there are obvious limitations such as it can not handle words not including kanzi ranking or preference of assigned codes is not obtained null not applicable to languages other than japanese
in order to overcome the drawback of the k nn approach the category based approach first makes a cluster for each category consisting of documents assigned the same category then calculates the similarity between a target document and each of these document clusters
in the first step we show that we need an incomplete but souild inli rence mechanism for lcxical disambiguation since a complete mechanisin leads to wrong results
the whole problem is now that despite of the de idability of mp the lexical disamtfiguation problem would still be undecidable if it wouhl pre sul pose a consistent discourse
lexical disambiguation is a procedm e determinin r for a le xically amhiguous sentence within a discourse which reading of the selttellce is contextually api ropriate
let us fllrthermore assume that we wouhl know that the given discourse is consistent we abstract here first fi orn the i rohlem that this t roperty is undecidable
it is necessary to disambiguate uhr clock watch but 1lot for knowledge retrieval or other NUM atural language processing tasks based on german alone
it allows us to decide for two alternative readings of the discourse which one is less contradictory to what is said consistently in the discourse and to our conceptual knowledge
NUM extract word co occurrences NUM define similarities distances between words on the basis of co occurrences NUM cluster words on the basis of similarities the most crucial part of this approach is gathering word co occurrence data
it may also fail to achieve success and be abandoned
note that the algorithm also has an exploration parameter which
pragmatic issues in handling miscommunication observations of a spoken natural language dialog
the methodology is experimental and assigns a complexity constant to each operator
if one scores NUM NUM and the other NUM NUM should we conclude the second one is really doing better
first of all given a test result such as bracket accuracy it is necessary to know the confidance interval
we will use statistical distributions to confirm this problem occurs and to find a solution to the significance problem
and if this assumption is not justified the whole test is not appropriate without testing on more specific phenomena
this indicates an imaginary real test namely the part of the test that really served to compare the parsing systems
a would have been treated slightly favorable without a human cheek since relatively more errors go undetected
they can be calculated with these items so there is no need to discuss every one of them all the time
null for a binomial process we mast assume that the chance on success is the same for every bracket pair
since treebanks have become available to researchers a wide variety of techniques has been used to make broad coverage parsing systems
figure NUM ext eriments on a japanese newsl aper asahi shinl un f NUM and NUM since all l he u dfial ility lwx k
this can ol en the way to integrate severaj n et hods deveh l ed for ci g including the insideoutside algorithm tot grmmnar learning or disam biguation into an hpsc framework
we will focus in particular on two major types of verbal differences observed in a corpus of bilingual french english procedural texts extracted from aircraft maintenance manuals
the used grammar onsists of just NUM rule schemata which are generated fl om principles and rewriting rules aim NUM default lexical entries given for each part of speech with NUM manually tailored lexical entries
realisations of arguments correspondences i etween conceptual roles and deep syntactic relations 1i iv are specified in the lexical entry of each verb and predicative noun
proper soes involve an exact repetition of some previous linguistic material and are analyzed as involving an anaphor which is constrained by the restriction that it be a segmental copy of its antecedent
in pa rticular we laitu that it is the order of events in a sequettce that is critical
the parser generates a set of parse trees each of which covers a part of the input sentence
if the predicates that form the condition of the rule are verified the remaining predicates are evaluated
it is important to note that the system paraphrases the noun phrase it understood using the translate predicate
the following are corpus examples containing time consmning parse problems
the functionality of a database blackboard is to provide procedures to insert remove and lookup feature structures
these strategies were extended to the other phenomena
since underspecifled feature structures represent unresolved disjunctions they are an adequate point of departure for generating clarification questions
NUM we then choose the parse whose count is substantially higher than the others
this research has been supported in part by a nato science for stability grant tu language
we have disambiguated these texts using a rule base of about NUM hand crafted rules
the parse corresponding to each token with the highest vote can then be selected
it can be seen that we can attain very good recall and quite acceptable precision with just voting constraint rules
the current implementation of the voting approach is meant to be a proof of concept implementation and is rather inefficient
when all constraints match the votes of all the matching parses are incremented by v
to illustrate the flavor of our rules we can give the following examples NUM
the same suffix appears in different positions in the morphotactic paradigm conveying different information as in NUM and NUM below
the columns for m NUM are presented in order to emphasize that drastic loss of precision for those cases
the work reported here is part of a fully implemented system called pro verb which produces natural language proofs from proofs found by automated reasoning systems NUM
a text structure constructed in this way is the output of our microplanner and will be transformed into the input formalism of tag gen NUM our linguistic realizer
assume set f a set g a subset f g i element a f i element b f
clearly although still in the same format this is no more an upper model object since set f is an upper model process not an object
instead of having to construct resource trees for apos the user of our system only needs to define a mapping from the apos to upper model objects umos
in this sense our rules work with such variables at the semantic level of the upper model and therefore differ from those more syntactic rules reported in the literature
in the case of rule a NUM for instance pivp2 c is a logical consequence of p1 c a p2 c
the following example illustrates this rule in particular how the decision made here narrows the choices of linguistic resources for both p and t as an argument of q
text structure is first proposed by meteer NUM NUM in order to bridge the generation gap between the representation in the application program and the linguistic resources provided by the language
what we need here is a mixture of jackendoff s and levin s insights several of levin s fill verbs can be both transitive and intransitive and some of the intransitive readings denote to become xed
semspec is still underspecified with regard to for example constituent order and lexical choice between near synonyms that have the same semantics with respect to sitspec yet differ in terms of style collocational restrictions etc
qince wrh denotations are complex enough to reflect certain parts of event structure they can be related to the notion of aktionsart the verb inherent features characterizing primarily the temporal distribution of the event denoted
this would depend solely on the relative probability of the unseen derived entries created by applying these two rules to fax
and in generation we assume that higher probability forms are preferred as a way of conveying a given meaning
thus the grammar writer was in effect required to consider both competence and performance when stipulating a rule
in other words trailer here is being regarded as a container or location rather than as a vehicle
the search for a fully productive statement of verb alternations has led to an increasingly semantic perspective on such rules
but in the attempt to define such subcases pinker is forced to make subtle and often unintuitive distinctions
this effect might have accounted for the relatively 10w productivity observed for the dance rule
we discuss modifications to the formulae which would allow for this in the next section
to make this clearer consider the use of the probabilities to drive interpretation in the case of a nonce usage
this is mainly due to the fact that verbs available in both languages do not necessarily cover the same part of the initial content
it is important to note that in many cases these french instructions can be paraphrased by sentences based on simple verbs
put representation NUM seminput action token illoc value imperative domain predicate p agent x patient x rolen x
in many cases the characterisation of attributes along the semantic opposition manner property explains the acceptability or inacceptability of the adverbial forms
by sing the proposed method the existing thesa urus was expanded to cower a large quantity el text
suppose the word sen i ouki fighter a is to be placed in the thesaurus
the results of a tl experiment confirm the corltril ution of viewl oints to the i ositioning task
we describe experiments that show that the concepts of rhetorical analysts and nucleanty can be used effectively for deternumng the most nnportant umts m a text we show how these concepts can be xmplemented and we discuss results that we obtained with a chscourse based summanzatmn program
japanese has four conditional particles to reba tara and nara which are attached to the end of subordinate clauses as described in NUM
in matrix clauses we can use either the mood of the description of fa cts or the mood of evidentials like conjectures judgment and so on
one of the most important matters of concern in tliese types of system is how we can fix ambiguities in semantic representations and fill uuderspecified parts of them
in this paper we will show that in instruction manuals the constraint of conditionals can i e used to identify the referents of zero subjects
as well as the sentence NUM for japanese native speakers the subject of the matrix clause of NUM should be a machine
and nara the tendency of use of the conjunctives gives us a couple of strong defimlts to resolve the zero pronoun in tit matrix clauses
NUM general ontology in manuals and prinmry constraints in this section we consider the general ontology which can be used in dl types of manuals
the derivation q then produces a translation t as the resulting sequence of terminal symbols included in the target cfg skeletons in q
a wildcard can be constrained with a head as in house and maison
what is the minimum number of examples or training data required to achieve reasonable mt quality for a new domain
in this case we know that the coverage of default patterns is always identical to l t
hideo watanabe designed and implemented a prototype mt system for pattern based cfgs while shiho ogino developed a japanese generator of the prototype
NUM japan registered a trade deficit of NUM million reflecting the country s economic sluggishness according to government figures
note the simplicity that results from using a notation in which users only have to specify the surface ordering of words and phrases
lea whose verl ex list matches in all but the last leaf w l tex ta bel we assign a label lill rence error of c
ijmn li m l af nodo i isertions rod nny itiitial stll string of x loilger tha n u ro ltfires nlore t ta n
this paper presents an efficient algorithm for retrieving from a database of trees all trees that match a given query tree appro imately that is within a certain error tolerance
most often exact matches for new sentences or fragments will not be in the database and one has to consider exampies that are similar to the sentence or fragment in question
table l properties of the synthetic databases of
frequent words are less informative of the sense and of the sentence similarity e.g. the appearance of this in two different sentences does not indicate similarity between them and does not indicate the sense of any target word
apos i the effects of corpus size and homogeneity on language model quality
it is theoretically possible to build a lm using the tiniest of corpora
the productions in pd define all the ways linear derivations can be composed from linear subderivations
however it is easy to see that this form of lig constitutes a normal form
however it is not always desirable to store all of this data
this representation captures the fact that the values of a stack of symbols is well parenthesized
since its production set is non empty we have a NUM ps l
the computation of the relations gives we can easily checked that this grammar is reduced
feaspar pertbrmed better than the lr parser in all six comparison performance measures that were made
lqn thermore the data sent by component a must undergo some moditication while being transported to colnl onellt l
null iloth ends are described using a conlignr tion lile that is read by the ils see below upon startup
for example price in figure NUM might insist with some probability that it depend on a verb to my right
pr preferences words t gs NUM i l twom i
to capture arity words probabilistically specify their ideal children as well fell is highly likely to want only one noun to its left
also one mistake may lead other mistakes making them not independent
states and probability contributions can be generated in any order as long as the summation for one state is finished before its probability enters into the computation of some successor state
for the purpose of exposition we will therefore ignore the technical complications introduced by these productions for a moment and then return to them once the overall picture has become clear
the operation of the parser is defined in terms of three operations that consult the current set of states and the current input symbol and add new states to the chart
where plt has a nonzero entry at x a iff there is a production for nonterminal x that starts with terminal a rl is the old left corner relation
the sum of probabilities that needs to be computed to arrive at the correct result contains infinitely many terms one for each possible loop through the t s production
similarly entry x y in the nth power p is the probability of generating y as the left corner of x with n NUM intermediate nonterminals
a similar queuing scheme with the start index order reversed can be used for the reverse completion step needed in the computation of outer probabilities section NUM NUM
so then word sense disambiguation is relative to the dictionary of sense choices available and can have no absolute quality about it
note thai both approaches are general enough to accommodate different assumptions about the applicability of lexicat rules to lexical entries i.e. they are compatible with both itypotheses a and b whether or not at given lexical rule applies to a lexical entry in van noord and l lcb ouma s al proach needs to be stipulated by the grarnmar writer who is in theory fl ee to use either a unification or subsumption test
the eviden lcb e training and candidate sele lcb tion ycle forms a l ootstrapi rcb ing t rcb rocess as folh ws crease recall of the spotter
when the speaker actually produces the utterance speech performance errors might occur resulting in the actual utterance that is to be interpreted by the listener
such a concept of linguistic description is attractive for several reasons NUM it supports the use of common formalisms and data structures on all linguistic levels NUM it provides declarative and reversible interface specifications between these levels NUM all information is simultaneously available and NUM no procedural interaction between linguistic modules needs to be set up
a bottom up hypothesis describes a passive edge complete subtree constructed by tile syntax parser and consists of the identifier of the rule instantiation that represents the edge and the completion history of the constructed passive edge
each parser then can reconstruct on its side the state the other parser is in how its chart or analysis tree looks like both parsers try to maintain or arriw at NUM another l roblem in incremental processing is that it is not known in advmme when an utte rance is tinished or a new utterance starts
this methodology reduces the size of tile structures for the syn parser to about NUM of the eom2this must be taken cu n qrano salis as it depends on how a specific grammar draws the line between syntax and semantics selecdonal constraints e.g. for verb arguments are typically part of semantics and are true constraints
on this view the syntactic constraints together with e.g. semantic selection constraints would constitute a subgrammar
type expansion can also be chosen to expand parts of a feature structure on the fly at run time
clearly completeness is not affected since we do not add further constrmnts to the sul grannnars
this t e havior is all outcome of our compilation schema namely cutting reentrancy points
this has the desired eft cot that we lo not lose the coreference consl raints and furthernlore are fl ee to expan l parts of the feature stru ture afterwards
NUM the user rejects the match that is offered
this allows one to use different name dictionaries for different texts
tile first formula increases weights for labels with support greater than NUM and decreases those with support smaller than NUM
NUM NUM sample case currency patterns in german
those two checks are kept exclusively disjunctive
there are two possible answers to this question been implemented in tel tk
interactive tagging proper nouns present another problem that falls under messy details
constraints can be of any order that is any number of variables may be involved in a constraint
these results are clearly better than those obtained at relaxation convergence and they also outperform hmm taggers
there seem to be two tendencies in this table first using trigrmns is only helpflfl in wsj
more detailed information about the performance of the classifier matcher and acquisition tool see below can be obtained from buitelaar forthcoming
for instance the event of increase as in increasing the communication between member states implies increasing both the act of communicating an object
for example asking how many re tangles a re hid letl tit of the canvast
if a feature head slash is passed to the verbal temple the finite verb is extracted allowing the governing phrase to realize it in first or second position
the two level rules could be taken over in l heir original form only l he morl hologieal tillers had to l e l ranstaled
a mechanism common to all arguments and thus incorporated into every macro expanding to an argument specification is the extraction mechanism required to handle movement see fig NUM
one of the daughters is the head of the phrase iiead dtit its head features are identical to the head features of the phrase llead feature principle
e.g. he default scracegy operates on ategory s in fig NUM as if cset subj pred had teen specitied
c ssing behavior is NUM o ilse idle cset a nd pattern special at t rib ul es of f u f in an asymmetrical fashion
it should be noted that this entry does not col respond exactly to the actual representation in the generator it serves simply to illustrate the basic ideas underlying the transformation
a plot of the improvement in the performance vs iteration number appears in figure NUM the success rate is plotted for each sense and for the weighted average of both senses we considered the weights are proportional to the numb er of examples of each sense
for instance in the training set for suit we would use in addition to the contexts of suit all the contexts of cour c and of clothes in the corpus because court and clothes appear in the mrd entry of suit that defines its two senses
the need for tagged examples creates a problem referred to in previous works as the knowledge acquisition bottleneck training a disambiguator for w requires that the examples in the corpus be partitioned into senses which in turn requires a fully operational disambiguator
for each item x the algorithm stops updating its similarity values to other items that is updating its row in the similarity matrix in the first iteration that satisfies rnaxyfi 2d y e where e NUM is a preset threshold
in addition there are plenty of exceptions in both directions rape pool grants code and premier are not necessarily nouns and sweeping leads bound and worry are not necessarily non nouns
the correlations for idf are perhaps somewhat larger than those for log NUM o2 suggesting that idf may be somewhat more robust which is not df boycott than for crummy keywords like somewhat
in this way the 0s can depend on an infinite number of unknowable hidden variables e.g. what the documents are about who wrote them when they were written what was going on in the world when they were written etc but we do n t need to know these dependencies for any particular document
both the poisson and binomial assume that the variance over documents is no larger than the mean and yet we find that it can be quite a bit larger especially for interesting words such as boycott where there are hidden variables such as topic that conspire to undermine the independence assumption behind the poisson and the binomial
although this rule inserts the second derive into another text structure the resulting structure is now a chain no longer a plain derive
it is desirable that the set eg vj be divided into disjoint subsets by the discovered pairs of cu and fa
this test was therefore applied to the word frequency lists of each of the domains in the bnc and the email corpus to identify which corpora were most similar
the correctness of the rules in this category with respect to the information conveyed is guaranteed by the semantics of the upper model concerned
NUM as a result the speaker has a negative emo null tional attitude toward the incongruity between what is expected and what actually is
in the case of 3a it alludes to peter s action of washing the dishes so that the rule for angry t emotion becomes more accessible
although this t aper does not describe how these praglnatic t rinciples shouhl l e formalized they should be taken into account for the next steps of our study
the experiments performed on a subset of u s
NUM use of mixed boolean soft retrieval model
the experiments were performed using smart system as baseline
for instance a user who has successftdly used the dialogue system on several occasions no longer needs to be introduced to the system but is capable of launching on the ticket reservation task right away
long phrasal terms are decomposed into pairs in two phases as follows
for example a trigram cut off of NUM implies that all the trigrams with frequencies of NUM or fewer in the training data are not used in building the model
for instance the fact that it is possible to make reservations of stand by tickets on international ilights may lead users to conclude erroneously that this is also possible on domestic lqights
taking the subset of rules relating to the coh n for example shows that there are NUM underlying rule patterns from the original analysis as shown in table NUM
for instance the pronoun f can create many active arcs relevant to the rules pronoun NUM np pronoun and s np vp which can be chained
NUM if the created passive arc satisfies the leftmost part of an uninstantiated variable in the pattern of neighboring active arcs the variable is instantiated with the passive arc and a new passive or active arc is created
thus the passive arc NUM has its source and target structure through the combination of NUM and NUM the total distance value NUM NUM and the head word goes
while language oriented modules such as morphological analysis and generation are provided to treat multi lingual translation the transfer module which is a central component is a common part of the translation system for every language pair
based on the results of distance cab culations other partial source patterns for NUM x to y and x a m are transferred to y ni NUM with the distance value NUM NUM and gozen x jt with the distance value NUM NUM
table NUM shows the translation time of the above sentences hi the above translations the same translation results could again be obtained for both methods llowever the new method can achieve a far more efficient translation than the average tramslation times in the top down method were NUM NUM seconds for a NUM word input and NUM NUM seconds for a NUM word input
the difference in total distance value between the two possible structures is due only to the distance value of x at f table NUM shows the results of the distance calculation in x at y for the combination of NUM and NUM and for that of NUM and NUM
the procedure r r applying constituent boundary patterns is perfomed after the assignment of morphological information to each word of an input string and is as follows a insertion of constituent boundary marker b NUM eriw tion of possible structures e structural disambiguation NUM y semantic dislance calculation
john sang but not in new york
if there is a phrase adjp filling an adjunct slot of the same type in a then leave adjp in this slot and remove adjp from s else b
NUM john sings and beautifully too
e generate a new syntactic structure as follows
the other cells show the rank correlation r and the value of n bnc refers to the complete corpus
the following rule makes it possible for np s like his paycheck to add dynamic individuals to the con
by contrast the bnc lm with the bnc vocabulary has an oov rate of NUM NUM and a correct of NUM NUM
in dynamic semantics the semantic structure of a discourse gives rise to constraints on the resolution of anaphoric expressions
involves an np atttece tent ontaining a sh ppy vp ellipsis switching froin drinks to gambles
since the dynamic theory treats vp ellipsis uniformly with np proforms xp and yp both range over np and vp
in the representations that follow we will often merge boxes without oltli lellt to silllplify representations
muskens shows that dr i boxes can t e viewed as abbreviations for expressions in ordinary type logic
dynamic a t tllti o slot l y id mtity
the phenomenon of paycheck pronouns NUM is illustrated by the following example NUM smith spent his paycheck
however the old to new ordering prim iple is a generalization to which exceptions can be found
if there is a pronoun in the sentence it ia likely to be the jb
further research is needed to disa mbiguate the use of the two possible word orders
further research is also needed on the exact role of verbs in the is
thus the conditions governing dropping and postposing are areas that require more research
they also rely on statistical data choosing the most frequently used word orders
in the mt domain this can be determined by the referential form in the source text
these simplifications allow me to test out the algorithms for determining the topic and the focus presented in this section
in this paper i discussed how to determine the appropriate word order using contextual information in translating into turkish
consider the predicate load NUM the peasant loaded the horses
the relewmt lexical entries for the fragnient were as follows
second a ps structure rule is introduced of the following sort
entries according to the system s internm algorithm of searching the rule space
this causes an unwelcome expansion of the lexical database and increases parsing time
horizontal redundancy is inherent to lexica consisting of descriptions of fnlly formed objects
this can be also exemplified from the domain of verb alternation phenomena
horizontm relations between objects are in principle pretty much unconstrained
NUM the peasant loaded tit horses with hay
optionality of pp complements can also be captured easily with this proposal
in cases such as these a knowledge of the particular topic can be used to advantage
more precisely the error rates are measured using two standard metrics percentage correct and accuracy
however the use of such iterafive techniques is not totally without precedent within the lm eornmunity
there were NUM NUM unique category patterns extracted fl om the corpus for the live most common marks of point punctuation ranging from NUM NUM for tile comma to NUM for the dash
in these tests the highest accuracy was always obtained by s type transducers either with all subsequences up to a length of two classes NUM or with subsequences occurring at least once in a corpus of NUM NUM words
to build an s type transducer a large number of initial class subsequences ci and extended middle class subsequences c n are generated in one of the following two ways a extraction from a corpus based on a lexicon and a guesser we annotate an untagged training corpus with class labels
null as soon as an input token gets labeled with the tag class of sentence end symbols fig
i am grateful to lauri karttunen and gregory grefenstette both rxrc grenoble for extensive and frequent discussion during the period of my work as well as to julian kupiec xerox parc and mehryar mohri at t research for sending me some interesting ideas before i started
NUM NUM computer ultra2 NUM cpu NUM mbytes physical ram NUM NUM gbytes virtual ram length of two classes s nl fst NUM NUM NUM NUM or with subsequences occurring at least once in a training corpus of NUM NUM words s nl fst look f1 NUM NUM
if the arc comes from the initial state the most probable pair of a class and a tag destination state is estimated by argrnkaxpl ci tih 7r tik b ciltik NUM if the arc comes from a state other than the initial state the most probable pair is estimated by
in which all middle subsequences s deg are still marked and extended in the sense that all occurrences of all unambiguous classes are mentioned twice once unmarked as cu at the end of every sequence ci or con NUM at the beginning and the second time marked as c u of every following sequence c deg
the correct translations of a word that has several correct translations will be assigned a lower probability than the correct translation of a word that has only one correct translation
an application that calls on the word to word model to link words in a bitext could treat unlinked words differently from linked words and avoid basing subsequent decisions on uncertain inputs
rors made by the word to word model and the ibm model NUM solid lines are links made by both models dashes lines are links made by the ibm model only
the direct association between uk and vk and the direct association between uk and uk l give rise to an indirect association between v and uk l
then the probability that u and v are linked k u v times out of n u v co occurrences is a mixture of two binomials
it is impossible to replicate the experiments used to evaluate other translation models in the literature because neither the models nor the programs that induce them are generally available
input NUM NUM mod poss th decorate template be ident thing NUM at ident thing NUM ed NUM with poss head thing NUM two thematic roles th and mod poss are specified for the above sense of the english verb decorate
particular attention is drawn to the top down predictive character of the linking relation and to its significance not only as a filter for increasing the efficiency of syntactic analysis but as a device for the top down instantiation of information which then serves as a key to the directed analysis of inflected forms as well as unknown or new words
for a thematic role with a required preposition lexicall consults a set of predefined mappings between prepositions or postpositions in a language like korean and their corresponding primitive representations s in the current case the preposition with is mapped to the following primitive representation with poss
the student s sentence is processed and the following lcs structure is produced NUM e cause the system identifies the student s response as a match with the prestored answer but it also recognizes that there is one piece of missing information and one piece of extra information
if this representation corresponds to a subcomponent of the lcs template the program recognizes this as a match against the grid and the marker is placed in the template at the level where this match occurs as in the entry for entrar given in NUM above
jack threw the book in the trash jack put the book in the trash extra manner you re assuming things jack is friendly jack put the book in the trash mismatch primitive please reread jack threw the book jack put the book in the trash missing argument where
for example mthough the lcs processor is capable of determining that the phrase in the trash partially matches the answer to where did john put the book a pragmatic component would be required to determine that this answer is perhaps more appropriate than the full answer he put the book in the trash
an example of input output for our acquisition procedure is shown here NUM acquisition of lcs for touch input NUM NUM th loc touch 2verbs not occurring in levin s book are also assigned to classes using techniques described in lcb dorr and jones NUM dorr to appear
if a preposition is required but it is not specified i.e. empty parentheses NUM then the marker is positioned at the level dominating the node that corresponds to that role which indicates that several different prepositions might apply as in put on put under put through etc
for example the mapping produces the following primitive representations for the english word to to loc at loc to poss at poss to temp at temp toward loc at loc toward poss at poss
the overall linking relation l is then defined so that b b e l iff NUM there is a x x l u l2 such that b u x and b m x exist or else NUM b u b exists the reflexive case where b satisfies the parse goal b
it was soon noted that the efficiency of the algorithm could be improved significantly through the use of a reachability or linking relation compiled out of the grammar before parsing this consists of the reflexive and transitive closure of the relation defined by the pairs of mother and lc categories specified in the set of syntax rules of the grammar
wealthy a republican ap and np p
er findet und hilft he find acc and help dat
several systems have used wor lnet a nd statls tieal infi rmation from large corpora NUM NUM NUM
rh next sub secti m h s ril es how viewlmints axe exl ra cted
there are several approaches which may eliminate the need for such systematic ambiguity
figure NUM a blocked lcg analysis of the ungrammatical NUM
figure NUM the lcg analysis of 2b
the following sections explore the empirical consequences of these two approaches
for example n des fi r biological words haw many levels while abstract words are classified in relatively shallow lew4s
the source of the words was artmes pu blished in a aa p umse ewspape r ni kkei st inbun iu NUM
in tile c se of the pattern tukau use NUM nodes with the s mm relationship tre extra cted
xpressions h r he word fightef th is the r h tii nsltips
this method uses only the linguistic constraints and the general ontology of the world described by manuals
if the subject is the speaker the verb in volitional use expresses speaker s volition
from simple electrical appliances to complex computer systems almost all machines are accompanied by instruction manuals
constraint NUM discourse situation speaker writer manufacturer hearer reader user
masu shows politeness milch is expressed by cot iu l
in contrast we may not use the expressions of volition requests and so on
shows successi lcb eness of two e eents observed in a real situation
for example the subject of the matrix clause of the following sentence refers to the users
therefore the only possible interpretation is that the subject of the matrix clause is the machine
as illustrated in fig NUM earlier this turns out to be a result of matching the unique but long mapping head d as in forehead itbr d arc li equency NUM in conjunction with the very common mapping sh j as in she and shed arc frequency NUM which swamps the overall score of NUM
in this section we investigate what is involved in extending dop1 in order to parse sentences that possibly contain unknown words
it is easy to establish the unknown words of a sentence but it is unclear how to establish the unknown category words
the method is of general interest since it shows that good performance can be obtained without the use of a part of speech tagger
on the table figure NUM different derivation generating the same parse tree for she displayed the dress on the table
for instance for the estimation of the population of bigrams the number of distinct unigrams is usually assumed to be known
the calculations for r NUM rest on an estimation of no the number of np subtrees that have not been seen
the mismatch method has one bottleneck the unknown words and unknown category words of a sentence need to be known before parsing can start
we must keep in mind that dop4 is a hybrid model where frequencies of subtrees are combined with a dictionary look up
e while it is dearly necessary to decide whether j is a child of i conditioning that iccision as alrove may not reduce its test entropy as mneh as a tnore linguistically perspienous condition woul
this worked fine in the examples tackled above but it is expected that a notion like o command ultimately defined on the basis of the precedence relation may need some further specification
contrary to the requirements of principle b in such contexts pronouns may be bound by an antecedent occurring in the same clause but only if it is the subject of the causative construction
maria talked to him ahout l edro i b
maria talked to pedro ahout him a
coverage the project adopted a corpus based approach
it would result in a desaster ibr efficiency
it is then possible to rank the matching patterns according to a linear ordering of the weights rather than the pairwise partial ordering of patterns described in the previous section
we conclude that the number of pairs totally instantiated by the algorithm is o pt t
such invalid spines are not deleted during the check of the lig conditions because they could be composed of sub spines which are themselves parts of other valid spines
NUM which is then to be applied recursiw ly to the clusters thus obtained
it is possible that with the descriptive power of these grammars and lexicons individual usages of words and phrases may be defined specifically enough to give correct translations
llc lex and rlc are n tuple of regular expressions of the form xl x2 xn
each ruh is associated with a feature structure which must unify with the feature structures of the affecl ed lexical entries
the filling of t by the spreading of the second iadical is achieved by the unification of c in lfx with c in rlc
eactl lexical entry is associated with a category and a feature structure of the form cat fs column NUM
measures NUM and NUM are derived in a similar fashion but undergo a rule of syncope as shown in NUM
kuttib measure NUM ktutib is derived by the aflixation of a lcb t rcb to the base template under np
for example if a joint distribution p x1 x x NUM over NUM random variables x1 x2 xa aoptiona NUM slots tre not necessarily independent but if two optional slots are randomly selected it is likely that they are indet endent of one a nother
NUM note in the above example that the case slot of from and that of to should be considered dependent and the attachment sit of one of the prepositional phrases case slots can be determined by that of the other with high accuracy and confidence
can be obtained by analyzing a simple set of parsed rces of each senten e in a text withom constructing a predse model of the contex tl rough deep senmntic anmysis
the number of parameters in modell model2 and model3 are NUM NUM and NUM respectiv ly while the number of dependencies are NUM NUM aud NUM respectively
atomic feature pairs belonging to the same branches have the same relation to all other branches
to train and run feaspar feature structure parser only limited handmodeled knowledge is required
said plaintiff and plaintiff s counsel
many of these are titles speaker turn indicators etc
NUM sentences of test data were utilized
boolean values for example are mapped to a single bit
this was achieved by having human coders read plain text versions of the parsed passages marking what they felt to be the antecedent
also newer versions of the treebank include semantic tags to adjunct phrases which will aid in preventing the misidentification of subdeletion described above
the problem category occurred when prepositional phrases and noun phrases in the antecedent verb phrases were unnecessary because of analogous phrases adjacent to the vpe
that is it assumes that prepositional phrases or noun phrases following the vpe always implies that like phrases should be deleted from the antecedent
the algorithm attempts to detect and solve subdeletion by locating adjuncts of similar types in a verb phrase ellipsis and corresponding antecedent
NUM categories of errors the most recent version of vpeal correctly selects NUM out of NUM antecedents from the brown corpus
the remainder of this paper will describe the categories of errors observed then describe an approach to reducing one category of errors
the expectation was that these counter examples would be less frequent than the cases in which the algorithm would correctly remove unwanted text
in this example two prepositional phrases to school and on friday follow the anteeedent s head verb drove
in these cases however human coders have selected text that is adjacent to but not parsed as contained by the verb phrase as part of the antecedent
beyond the ambiguities that proper names share with common nouns some ambiguities are particular to names noun phrases may be ambiguous between a name reading and a common noun phrase as in candy the person s name versus candy the food or the house as an organization versus a house referring to a building
the man may refer to more than one male individual previously mentioned in the discourse or present in the non linguistic context j smith may similarly refer to more than one individual named joseph smith john smith jane smith etc semantic ambiguity of names is very common because of the standard practice of using shorter names to stand for longer ones
each linked group is categorized by an entity type and assigned a canonical name as its identifier
the canonical name is the fullest least ambiguous label that can be used to refer to the entity
after the whole document collection has been processed linked groups are merged across documents and their variants combined
however our goal has been to design nominator to function optimally in the absence of such a resource
without a database names need to be discovered in the text and linked to entities they refer to
a set of heuristics is applied to determine whether each candidate name should be split into smaller independent names
the final stage is merging the parsed and tagged text with all the annotation sgml like markup header information for return to atr
xanthippe retains control at all times during the tag correction process for instance allowing the insertion only of tags valid according to the atr
to illustrate the diverse nature of the documents included in this treebank we list in table NUM titles of nine typical documents
our algorithm first learned a transducer whose decision tree is shown in figure NUM
both of our initial augmentations of ostia to bias it toward phonological naturalness improve performance
inferring finite state transducers seems to hold promise as a method for learning phonological rules
table NUM shows the results of this investigation
at least five paragraphs hoping that the mtersecuon of the two manual summaries roll indeed yield the most important paragraphs m an artscle the articles used m our evaluation had anywhere between th n teen and forty eight content paragraphs the current implementation of the smart system also considers the section headings etc as ndlvldual paragraphs such paragraphs were marked as non content and were gnored m the summanzatmn process
in tlus study we have tried to evaluate automattc summanzauon methods proposed earlier if a good testbed for evaluaung summaries were available the evalualaon methodology adopted m this study could be improved but we believe it is the best we can currently do under
we are deeply indebted to late professor gerard salton for all hts guidance during the lineal stages of this work without the invaluable advice and support of professor salton this work would not have been possible we thank nawaaz ahmed david fielding nicholas howe s ravtkumar cyntlua robinson and dlvakar vlshwanath for generaung extracts for the arucles used m the evaluauon process
opment to telecommumcattons the four kinds of services me netwerk
the constraint templates in the lcb w h rcb models were
in a transducer this information is represented in the current state of the transducer
the relaxation algorithm consists of start in a randoln weighted labeling
that is in the same conditions than hmm taggers
iterate the process until a convergence criterion is met
support flmction NUM NUM is slightly ahead NUM NUM
we improved performance adding few constraints which were not linguistically motiwtted
the non terminal symbols in v are triples denoted a where a e vn and p and q are states
because of these NUM rot erties the correctness of our parsing nmthod is guaranteed
however it is obvious that our method is much fi ster than the naive one
the delayed evaluation is considered later when tile non head dtr values are instantiated by an actual sign
a state in an la corresponds to a phrasal sign suc h as sj and NUM
then unifying those non head daughters with actual signs constructed from input parsing can be done
NUM np wrote a good paper figure NUM a parsing example
however this simple la generation algorithm has a termination problem
the knowledge about the linguistic context of nouns in the corpus that is collected by the pattern matcher is now used to classify unknown nouns
this assumption is fundamentally different from the design philosophies behind existing lexical semantic resources like wordnet that do not account for any regularities between senses
under lud scoping alfa and loq conditions are found
it may signal a new topic or the return of an old topic the speaker s attempt to hold the conversational floor
semantically the meaning is restricted to explanation
hurnan inade abstracts we computed precision recall for very human subject compared to all the other NUM subjects taking the average precision recall
it covered its small corpus relatively completely and described the necessary phenomena relatively fldly lilt was a primary goal of this line of research to begin meeting the above criteria for semantic coverage
many thanks to yorick wilks for his constructive criticism
a useful measure of semantic coverage must involve measurement along each of the three dimensions with respect to correctness or success rate and efficiency or speed
it is not rlear that statistical information acquired tbr one probleln such as sense disambiguation is of use in hmtdling other problems such as processing non literal expressions
in this first attempt at a qualitative metric we list questions relevant for assigning qualitative tendency scores to an nlp system to measure its semantic coverage
such concentration of effort will allow knowledge acquirers to have spend a fraction of the effort that must go into building a general machine tractable encyclopedia of knowledge and yet to attain significant coverage of language phenomena
when estimating their profiles we thought either about some representative systems belonging to an approach or thought of the properties of a prototypical system in a particular paradigm if no examples presented themselves readily
a pntative nia system based on the yc project has been selected as a prototyl e for systems not devised h r a particular application
for translating spoken language an analogical system should have various sentence stems and patterns along with their corresponding translation in its example database
lqrsl wo acquit d l hc sloi bascd case flame palioi is for ill of t he NUM verbs
table NUM shows the results of these experiments where dendroid stands for the former method and independent the latter
NUM for the sk t based too m sometimes case slots are found to i e del endent
size used in estimation and the relationship betweett the ki distance between the estimated and true modols and the data size
tree in fig NUM three additionm trees are admitted for sentence NUM in which the slasii value originates in one of the pre terminal nodes for the anxiliaries
thus even if the lr would be reformulated to apply only to auxiliaries the following ungrammaticm sentence could not be excluded parser werden verstanden zu implementieren
the car was bought can in NUM the auxiliary k6nnen has been passivized and the direct object of the transitive verb has been promoted as the subject of k6nnen
it would go beyond tim scope of this paper to present a fljly worked out proposal on how to i ro cess lrs in a coml utational system for iipsg
in meurers and minhen s approach the deduction rules for automatically generating a finite state encoding of lexical rules can likewise be based on a subsumption check or a unification clieck
each i r is then viewed as a two place definite relation as illns rated in fi r NUM for the l pl t lcb of fig l
common to these two proposals is the idea of treating lrs as ltorn clause constraints on lf s of the kind shown schematically in fig NUM
the analysis of topicalized sentences that contain auxiliaries could originate in the le for the main verb but also in the le for each auxiliary present in the sentence
ii shiharai wa ginkou fllrikomi o o machishite oriinasu
such expressions are used depending on social roles
underspecified feature structures represent sets of feature structures efficiently in that they express both the information that is common to and the information that differs between the feature structures in question
the type hierarchy allows us to express the is a relations in the following noted in cursive letters and is part of relations noted in capital letters that hold between objects
the application of this formalism to swahili has been under process since NUM and it has now after having been tested with a corpus of one million words reached a mature phase with a recall of NUM NUM in average running text and precision of close to NUM
for example the context condition NUM n link NUM pp on link NUM adj reads there is a noun n on the left followed by pronoun pi lcb on followed by and adjective adj
mu m mir mu NUM NUM sg n mi m mir mu NUM NUM pl n this is a sub lexicon with the name m mi containing prefixes of the noun classes NUM and NUM each entry may have three parts but only the middle part is compulsory
ill this l aper we also tel resent an agent x s mental situation as ux and his lmr beliefs as supl ort relations t etween ux and infons
antecedents selected by vpeal were considered correct if they matched the antecedents selected by the coders
NUM discontinuous antecedents the correct antecedent is split into two parts NUM cases
empirical evaluation of the algorithm indicates that a purely syntactic approach to detecting subdeletion is probably insufficient
a prepositional phrase on saturday also exists following the vpe s head verb
the algorithm makes no provisions for cases containing multiple prepositional phrases and noun phrases
this algorithm will correctly handle the NUM cases of subdeletion in the brown corpus
sarcasm and irony we argue that explicit victims and disl lay of the speaker s counterfactual pleased emotion described in section NUM NUM m e dist inctive prop rties of sarcasm
NUM NUM l as buch wird peter gekauft haben kgnnen
building our draft lag tool therefore required us first to determine how to represent the model of a task and then to build tools for creating and manipulating this model
lqgure NUM passive lcxical l lcb ules for german kiss NUM
within this component one specifies so called tsas text structure to linguistic structure rules which transfornl the th output into 3to extend the matches the user would need to change the regular expressions
s w features is a list of feature value descrip tions of the tag s features tag content is tile atomic content of tile string within the tag optional in the lift rule
this allows a considerable improvement of parse time since some information is already instantiated before the parse starts
when tagit matches a pattern against tim input the matched string is replaced with an appropriate usr tag
the text examples are especially problematic given the german method of expressing the ones digit before the tens digit e.g.
an ascii text will first go through a processing chain consisting in a sgml based tagging of the elements of the input
person names can contain initials and they might be modified by titles mr or functions business names can be modified by some standard terminology like ltd
the output from those processes consist of the input decorated with tags for the recognized elements p for paragraphs s for sentences w for words in case of morphological analysis the tag NUM is provided for morphemes and pt for punctuation signs
the interaction with the user and the dictionaries will provide a way to tune the effect of these expressions
in order to deal with this phenomenon regular expression patterns describing the currency mnounts were defned in awk
the realization rule on each of these inserts this into the structure the relationship of class being shown by a colon
on re entry to the network to fill out the lower part of the tree however this choice will be reversed
it is shown as both an element of structure in the unit above and by the name of the unit that fills it
it is these that express the likelihood that the satellite will precede or follow the nucleus as the sp rules below the network show
by organizing summations NUM and NUM so that NUM are first summed by lhs nonterminals the entire completion operation can be accomplished in NUM NUM
these texts were derived from a stock market bulletin written in japanese and its abstract written in english which were distributed electrically via a computer network
in order o develop our approach to lexical disambiguation we work successively through some
these restrictions allow one to base retrieval on a tractable logic which is sound and complete
not nmore complex examples can be found e.g. in kalnp NUM kamp and rofldeutseher 1994a b
we were then able to determine the m satisfiahility of the discourse and mp by resolution
for our conceptual knowledge on the other hand we make the much stronger assumption 1i
since our conceptual knowledge on the other hand can be ret resenl e d
we can distinguish two classes of approaches surfaceoriented approaches and inference based approaches
the most promineut inference pattern which is also the center of the discussion here is e.g.
the only difference is that lexical disambiguation requires a little bit more understanding
in this table sentno and coincidence represent which senfence the string appeared in and how many characters are shared by the two adjacent strings respectively
such detaih d collocations r tillicult NUM o hand conlpile the automatic extra tion of bilingual collocations is needed
NUM NUM domain codes thesaural categories
sense tagging semantic tagging with a lexicon
we are grateful to jim cowie at crl for providing the simulated annealing code and to kevin humphries and hamish cnnningham of the sheffield i nlp group for advice with the implementation of the tagger
finally we note from figure NUM that the realisation of sub steps is heavily loaded in favor of imperatives in procedure
these results however were obtained from a corpus of instructions mostly for domestic appliances as opposed to software manuals
we continue to explore further situational and contextual factors which might allow a system to fully control its available linguistic resources
as far as the polarity system is concerned negation is effectively ruled out for function goal and substep
we do not make this distinction in the linguistic analysis and regroup these related task structure elements under one label
the fact that the translation is adaptive rather than literal gives us confidence in using this manual for our analysis
on the dimension of modality the emphasis is on personal possibility rather than obligation and on inclination
it will also allow us to determine the text structures appropriate in each genre a study we are currently undertaking
in example b the same branch office chief position phrase provides part of an organization name in one example while it acts as a context for a person name in the other
system based on the fastspec pattern specification language
the headline slot has a sequence of sentences
figure NUM ambiguous context examples ie customization of dictionary
during NUM NUM the english fastus infrastructure underwent a number of changes the most significant of which was the transition from grasper to a declarative pattern specification language called fastspec
to recognize name strings for organizations persons locations dates times money and percents the met japanese fastus produces a template entity for each
the sgml handler is written in fastspec so it can be easily adapted to text tagging formats other than sgml as well as to more complex text structures containing sections subsections and tables
when the list is too large disambiguation requires almost as much effort as if there were no list but certain names elude predictable internal name patterns so need to be known a priori
there are essentially two methods for coping with this
disambiguation relies on the linguistic context
bear in mind that there exists the lexical entry in NUM to which the tl rules above map to
this makes that uon unflautung stems have to be left unspecified for umlautung as otherwise st could not attach
there is no point in nmltiplying syntactic entries by their semantic ambiguities and make all of these entries available for analysis
in the systenl described here they exist as nlacros and they are spelt out in category specific word and phrase structure rules
as on the other hand lexicalist theories result in an abundance of lexical ambiguities refinement is a relief
last not least tuning the grammars with a view oil efficiency has contributed to tile current performance of the system
syn cons phenol umlaut none i the morphologically relevant information is encoded in cons
the tlm component deals with most major morphographemic variations occurring in german such as umlautung ss fl alternation schwa instability
a o it showed the necessity to devlop a th component and separate out specific phenomena from the treatment in the grannnar
this local neighborhood consists of the parse node itself its two left siblings its two right siblings and its four immediate ancestors
unlike more common decision tree classifiers which simply classify sets of conditions statistical decision trees give a probability distribution over all possible outcomes
a common approach in non statistical natural language systems is to bridge this gap by introducing intermediate representations such as parse structure and pre discourse sentence meaning
the content of the information does not necessarily reflect the position or the policy of the government and no official endorsement should be inferred
we wish to thank robert ingria for his effort in supervising the annotation of the training corpus and for his helpful technical suggestions
the discourse history h simply consists of the list of all post discourse frame representations for all previous utterances in the current session with the system
specifically each parse node indicates both a semantic and a syntactic class excepting a few types that serve purely syntactic functions
the full space of possible parses t is searched for n best candidates according to the measure p t p wit
in order to assess the performance of the t l several data sets were used
the dags that are unifyable with the filter dag meet the imposed restriction
medical patient reports consist mainly of free text combined with results of various laboratories
full forms in the lexical database which is some NUM non inflected forms
these eases of double analysis are grouped in the class NUM
if an ambiguous word is encountered its position is kept on the agenda
as a feature based formalism with a syntax modeled on patr it would be reasonable to expect that datr can be used to describe directed acyclic graphs dags in a patr like fashion
but if the mood of the form is passive then the part of the subcategorization frame that deals with objects and complements is stripped of its first item i.e. its direct object
orthogonal networks allow a node to inherit from more than one other node but the properties it inherits from each must be disjoint so that again no conflict can possibly arise
4s indeed we suspect but have no proof that every consistent datr description is extensionally equivalent to that is defines the same extensional sentences as a functional one
but these nonsensical theorems will be of no concern to a datr invoking nlp system that is able to specify in advance which paths are of interest for dag construction and to ignore all the rest
indeed this is the intuitive warrant for the use of the quote notation the quotes turn an inheritance descriptor into a kind computational linguistics volume NUM number NUM of value
exactly what happens to such an improper description in practice depends on the implementation and usages of this kind can be the source of hard to locate bugs see also section NUM NUM below
consider an analysis in which we put the common properties of verbs at a verb node and the disjoint common properties of words that take noun phrase complements at an np arg node
consequently the problem of extracting lexical probabilities from a small training corpus is twofold first the statistical model may not necessarily represent the use of a particular word in a particular context
bootstrapping from already tagged text this technique generally consists of using a small tagged corpus to train a system and having the system tag another subset of the corpus that gets disambiguated later
genotype bigram tagging frequency p r imp nmp p jmp p nmp in a similar way the statistical fst contains paths for unigrams and trigrams
among all parts of speech there is a clear division between closed class parts of speech which include prepositions and conjunctions and open class ones which includes verbs nouns and adjectives
genotypes play an important role for smoothing probabilities
NUM NUM contextual probabilities via bigram and trigram genotypes
the sub fst that corresponds to this bigram of genotypes will have p r jmp nmp on its input and all NUM possible taggings on its output
similarly if a trigram contained a bigram as a sub fst typically the cost of going through the trigram would be smaller than the cost of going through a bigram and a unigram
table NUM the most frequent open class genotypes
each tagging sequence has a different costs
and types for generating a clarification question and maps them to strings as shown in section NUM NUM NUM it also determines the top level slots corresponding to the expected answers which are accessed with the predicate gettranstoplevelslots0
pragmatically speaking however whethcl the obtained thesaurus agrees with our intuition in itself is only of secondary concern since the main lmr pose is to use the constructed t hcsaurus to help i uprow on a disaml igual ion
finally the algorithm has simple extensions for processing partially bracketed inputs and for finding partial parses and their likelihoods on ungrammatical inputs
wherever the original parsing procedure sums probabilities that correspond to alternative derivations of a grammatical entity the summation is replaced by a maximization
delete all null productions except on the start symbol in case the grammar as a whole produces c with nonzero probability
intuitively it is also true that a parser that performs these transitions exhaustively is complete i.e. it finds all possible derivations
in particular earley parsing is more efficient than the bottom up methods in cases where top down prediction can rule out potential parses of substrings
we say that a path starts with nonterminal x if the first state on it is a predicted state with x on the lhs
a stochastic context free grammar scfg extends the standard context free formalism by adding probabilities to each production p
the results for the other distorted sentences are shown in table NUM
in order to evaluate performance of the corrector NUM sentences were chosen at random from the journal computer science abstracts referativnyj zhurnal vychislitel nye nauki in russian
conventional hypertext is static in the sense that even though each reader may sample different parts of it once a hyper document has been constructed by its authors its content and form do not change
apart fi om the previously explained symbols we will make use of the following symbols in the next regular expressions NUM o i i me union of all left brackets for empty upper
many thanks to our collegues at parc and rxr c grenoble who helped us in whatever respect particularly to annie zaenen jean pierre chanod marc dymetman kenneth beesley and anne schiller h r helpfifl discussion on different topics and to irene maxwell for correcting the paper
we obtain these four different applications of context constraints denoted by i1 and v by varying the order of the auxiliary relations steps NUM to NUM described in section NUM NUM NUM cf
the resulting transducer maps upper inside an irtput string to lower when upl t i is between l l l and tlight in the input context leaving everything else unchanged
ehn ar NUM i he relation mal s every bracketed i jl l l i i ui i for non empty ui pei lcb and i i for empty uppi i to the corresp i leaving everything else unchanged
note that ui pi r low t lcb i i ft and ihgtit ui li li and ri stand for regular expressions of any complexity but restricted to denote regular languages
we can implement this by first attempting to apply a rule color not dark and then attempting to apply a rule concrete not heavy to any unresolved cases table NUM
shell and side are also relevant to the sense of the adjective but even when disambiguated themselves further information about the context of light shells and right side is required before the sense of the adjective can be resolved
in these cases however the reason the omitted text is not contained by the antecedent verb phrase is that an interposing phrase in the example above the vpe itself occurs in the middle of the antecedent
variables ranging over underspecified feature structures are indicated by curly brackets as in lcb obj path rcb NUM
suppose the user utters show me how i o get to the museum
if there are less than four objects the second rule will be evaluated
links exist between the representations in order to access the representation across the levels
for eilicicnt processing of ww to entail correspond ing eomplexity fin natural lmlguages that license cross serial dependencies hinges crucially on there being eflmently mlputable hoinonmrphisms tmtween the natural language and the string duplieati m languages
finally the regular languages ps3 are those produced by regular grammars characterized by rules that have a single nonterminal symbol on the lhs and on the rhs either a terminal symbol or a terminal and a single nonterminal
alt should be noted that this paper presents a simplified account of the quantification scheme used in semnet
these rules would produce either a deputive reading where although the speaker is a beneficiary of the action the recipient is unspecified or a reading where the speaker is also the recipient of the transfer action
blocking can be treated as a special case of this principle if speakers use higher frequency forms to convey a given meaning an extended meaning will not become conventionalized if a common synonym exists
similarly in figure NUM we assumed a use substance lexical rule but a more accurate estimation of probabilities is obtained by considering specialized subclasses as we will see in the next section
in addition since the probabilities encode the relative likelihood that a given word form will associate with a particular lexical entry the set of probabilities on states of a fsm will not be globally normalized
although many lexical rules are subject to exceptions gaps and variable degrees of conventionalization most are semi productive in the sense that they play a role in the production and interpretation of nonce forms and errors
but whatever the cause blocking is allowed for automatically by the approach proposed here since the probability calculated for unseen entries of high frequency words will be very low see section NUM
this ratio is obtained by dividing the ratio representing the productivity of the lexical rule s by the sum of the ratios of the lexical rules required to construct all the unseen entries
event e event e e arg l arg j create transfer lexeme fsm trans NUM NUM to ditrans NUM NUM
sul je ts were s lectcd for tl c verb like and the verb like was r n h r d lit r nlly in th translations with mm withont context
on the other hand tgl sublanguage grammars can be developed using existing resources
null first the word is looked for in the lexicon
similarly it c reates opening and losing actions for all windows
we address the tirst by providing reels that allow technical authors to buiht richer interface models
in this way the author can modify the underlying knowledge base while working from the text
in some cases the writer will decide to modify the generated text rather than tile underlying knowledge
the result is a procedural hierarchy such as the one shown in figure NUM
the author is required to specify only the main user goal action and the three plan nodes
if this fails it is looked for in the guesser
the joint probability of c and t can be estimated by
among the failures were NUM i see some stamps on the desk
theorem NUM let a cfg g be a set of source cfg skeletons in t
we therefore apply heuristic measurements to identify the most promising patterns for generating translations
proposition NUM let a cfg g be a set of source cfg skeletons in t
two cfgs g and h define the range of cfl l t
unification of a local feature with obj succeeds since it is not bound
NUM it is bett r NUM o learn preferences mltom tic lly in sl a t of specifyillg preferences by user
thell from lhe preference NUM john had l he telescope el lime NUM frolll lhe t reference l lohn is not equm to the mau
in f rioritized fircumscril tion we can livide preference rules into hiernrchy and this hierarchy giv s a t riority relation ov r
john just saw a man with a telescope
usage of circumscription for interpretation of pronominal anaphora
this configuration is done by simply editing a comiguration file and selecting the keywords yes or no for each cornl onent
we describe the integration of this communication facility in two versions of a speech to speech translation system which ditfer with regard to quality and quantity of data
verbmobil the primary application for which ice was built aims at developing an automatic interpreting device tbr a special type of negotiation between business people
using the gui the user can watch the operation of the whole system control the behavior of the components and monitor the datafiow between the components
the system structure reflects a set of components that communicate bilaterally without the involvement of a central mechanism or data structure that participates in every comrnunication event
this information includes names and locations of all components participating in the sys null tern as well as an overview about all channels currently established between components
the creation of a channel is done in two phases first any of the endpoint components sends a channel creation request to the ils
it encodes all NUM NUM NUM grams of genotypes extracted from the training corpus with a cost determined as a function of the frequency of the genotype decision in the training corpus
words are not estimated individually given their class categories rather genotypes are estimated separately from the words or in the context of other genotypes bi and tri gram probabilities
in an attempt to capture the multiple word ambiguities on the one hand and the recurrence of these observations on the other we came up with a new concept called genotype
the bigram in the example p r jmp nmp occurs NUM times in the training corpus
if this hypothesis is true for english we show that it does not hold for languages for which publicly available tagged corpora do not exist
given the problems created by estimating probabilities on a corpus of restricted size we present in section NUM a solution for coping with these difficulties
NUM study morpho syntactic ambiguity and word frequencies part of speech ambiguities must be observed as a function of the word frequencies as shown in section NUM
we believe that concentrating efforts on these issues will allow part of speech tagger developers to optimize time and effort in order to develop adequate basic training material
the knowledge of such dependencies is useflfl in various tasks in natural language processing especially in analysis of sentences involving multiple prepositional phrases such as the girl will fly a jet fl om tokyo to beijing
thus it is likely that the target joint distribution can be approximated reasonably well by the product of component distributions of low order drastically reducing the nuniber if paralneters hat need to be considered
in a morphologically inflected language this argument is particularly serious since a word can be tagged with a large number of parts of speech i.e. the ambiguity potential is high
each word has a genotype or series of tags based on morphological features assigned during morphological analysis and words according to their patterns share the same genotype
the assumptions of the formalization are the following
figure NUM outline of the rhetorical parsing algorithm
in other words we assume that the texts that we process are well formed from a discourse perspective much as researchers in sentence parsing assume that they are well formed from a syntactic perspective
in contrast sumita et al use deep syntactic and semantic processing techniques for determining the markers and the textual units and use sentences as minimal units in the discourse structures that they build
otherwise we assigned it a sentential usage
the rhetorical relations that the cue phrase signaled
consequently if one is to build discourse trees for unrestricted texts the problems that remain to be solved are the automatic determination of the textual units and the rhetorical relations that hold between them
NUM with its distant orbit lcb NUM percent farther from the sun than earth rcb and slim atmospheric blanket NUM mars experiences frigid weather conditions
the spearman correlation coefficient between the ranks assigned for each textual unit on the bases of the discourse trees built by the two analysts was very high NUM NUM atp NUM NUM level of significance
for example when an although marker is found a new clause whose right boundary is just before the occurrence of the marker is created
ps1 NUM denotes the class of languages generated by indexed grammars
rules of the form NUM are copy operations
no known syntactic phenomenon requires greater than indexed language expressivity
they are ehm aeterized by unrestricted grammar produc null tion rules
cross serial dependencies in dutdl and swiss german are the only known extracontext fi ee natural language syntactic phenonmna
this is relal ed to other work on reduplication phenonmna in formal models of computation
in fact we argue in the next section that ww is fairly straightforward to process
an edge with no expectations is inactive saturated and one with expectations is active
teas mable to enterta in the assuml tion that somel hing such exists
the rule format makes clear that it is less expressive than indexed grammars when interpreted directly
when xi assumes a word or a special symbol NUM as its value we refl r to the corresponding model pv xi
in the simplest case one could add top level productions s xs where x can expand to any nonterminal including an unknown word category
NUM like an earley parser lr parsing uses dotted productions called items to keep track of the progress of derivations as the input is processed
we simply remark that it should be possible to define interesting nonstandard probabilities in terms of earley parser actions so as to better model non context free phenomena
if the input comes with partial bracketing to indicate phrase structure this NUM this connection to the ghr algorithm was pointed out by fernando pereira
this is because states corresponding to later expansions always have to be completed first before they can lead to the completion of expansions earlier on in the derivation
for an application using a fixed grammar the time taken by the precomputation of left corner and unit production matrices may not be crucial since it occurs offline
the sample string can be parsed as either a aa or aa a each parse having a probability of p3q2
by assumption the grammar contains no nonterminals that generate c NUM therefore we must have NUM NUM c e q e d
for example if the input symbols themselves have attached likelihoods these can be integrated by multiplying them onto a and when a symbol is scanned
as new states are generated by prediction scanning and completion certain probabilities have to be accumulated corresponding to the multiple paths leading to a state
lexical rules under this approach are part of the theory just like any other constraint of the grammar and they relate the word objects licensed by the base lexical entries to another set of well formed word objects
frame specification becomes slightly more difficult when one considers type specifications of those paths in words serving as input to a lexical rule that occur in the out specification of the lexical rule but are not assigned a type value
NUM we will show that the detection and specification of frames and the use of program transformation to advance their integration into the lexicon encoding is one of the key ingredients of the covariation approach to hpsg lexical rules
checking the grammar this way indicates which c rules will not appear in some subtree
for every ego the pre or the post context may be empty
because the subsumption constraint in the lcg analysis is associated with the predicate argument relationship rather than the coordination construction as in the feature based subsumption account an lcg analysis paralleling the one given in figure NUM does not exist
if the subject is the hearer the st eaker expresses his her expectation that the hearer makes a volitional action shown by the sentence
NUM c c kono botan o osu to c n m this button ace push to ca de mas u
this paper proposes a method of the zero pronoun resohition which is one of the essential processes in understanding systems for japanese manual sentences
we focus on coordination phenomena because this is the one area of the grammar where underspecified agreement features seem to play a crucial linguistic role and can not be regarded merely as an abbreviatory device for a disjunction of fully specified agreement values
then each of the follow we would like to thank bob carpenter pauline jacobson john maxwell glynn morrill and audiences at brown university the university of pennsylvania and the universitpst stuttgart for helpful comments on this work
proper noun candidates are identified by means of regular expressions these being then rejected or accepted on the basis of run time interaction with the user
in the feature based account this is because the features associ null associated with each conjunct while in the lcg account the features associated with the complement specification in a predicate must subsume those associated with the complement itself
the default setup of the system defines the following processing chain the text is first converted to an edif eurotra document interchange format format
a path is complete if the last state on it matches the first except that the dot has moved to the end of the rhs
thus in lcg there is no principled reason not to assign a category an apparently contradictory feature specification such as np nom acc this might be a reasonable lexical category assignment for an np such as kim
we then show that the resulting analysis favourably compares with two prominent theories of focus namely rooth s alternative semantics and krifka s structured meano ings theory in that it correctly generates interpretations which these alternative theories can not yield
to determine the meaning of only likes mary the fsv of the vp nmst be known ttence the following equation lnust be solved gd o by tiou the value of id is then2
in particular the theory requires that a focus operator combines with a syntactic constituent c whose structured se mantics c gd f provides the focus NUM this operator associates with
for instance the semantics of only likes mary in 5b is not determined by the semantics of its parts but is instead identified with the semantic value of its antecedent only likes mary in 5a
it is well known that for higher order logic e.g. the typed a calculus the space of solutions can be infinite and furthermore the hou problem is only semi decidable so that tile unification algorithm need not terminate for unsolvable problems
finally we discuss the formal properties of the approach and argue that even though hou need not terminate for the class of unification problems dealt with in this paper hou avoids this shortcoming and is in fact computationally tractable
note that the computation of tip is unchanged since y already includes an infinity of cyclically generated subtrees for y where appropriate
while rules reported in the literature typically aggregate clauses our rules operate both above and beneath the level of clause constituents
in order to carry out appropriate textual rearrangement we need a representation formalism which allows flexible but principled manipulation of linguistic resources
first naturally occurring proofs contain paraphrases with respect to both rhetorical relations as well as to logical functions or predicates
t ascription NUM circumstantial class ascription figure NUM a fragment of upper model in proverb text sentence clause
while the former should be paraphrased to increase the flexibility continuity of the latter helps the user to identify technical concepts
while the overall planning mechanism is similar to the rst based planning approach the plan operators resemble the schemata in schema based planning
we use semantic grouping to characterize the merge of two parallel text structure objects with the same top concept by grouping their arguments
this textual operation eliminates one of the duplicates of f this section is devoted to various textual reorganisations which eliminate such redundancies
rule c NUM below addresses the problem that every step of derivation is mapped to a separate sentence in the default verbalization
concepts subset f lcb object while concepts set class ascription object
the model was also used to induce a translation lexicon from a NUM word corpus of french english weather reports
the value of class based models was demonstrated by the differences between the hidden parameters for the two classes
the competitive linking algorithm is designed to overcome the problem of indirect associations illustrated in figure NUM
our model can link word tokens in parallel texts as well as other translation models in the literature
our unitied theory of irony claims as an mmwer to q1 that irony is a figure of sl eeeh that inq lieitly displays the fact that its utteraime situation is surrounded by ironic environment
for example utterances NUM NUM and 2e tbr exampie NUM are not ironic even when they are given in the situation surrounded by ironic environment NUM NUM and 2e directly express the speaker s expectation and tile st eaker s emotional attitude rest ectively and both do not include pragmatic insincerity
the aim of this paper is to propose a unified theory of irony that answers to three crucial questions in an unequivocal manner q1 what are properties that distinguish irony from non ironic utterances q2 how do hearers recognize utterances to be ironic and q3 what do ironic utterances convey to he rers
finally the notion of the opt osite of the literal meaning is problematic because it is aplflicable only to declarative assertions hut many ironic utterances can take non declarative forms questions such as lb requests such as 2b offerings such a s le and expressives such as 3a
n many ases an ironic utterance is praglnatically insincere ill the sense that it intentionally violates one of the preconditions in figure NUM i.e. sincerity preparatory and propositional conditions that need to hold before its illocutionary act is accomplished but pragmatic insincerity also oecurs when an utteranee violates other praglnatic l rineiples
we have adopted the simple heuristic that the model has converged when this probability stops increasing
these authors kindly provided us with the links generated by that model in NUM aligned sentences from a heldout test set
thus if there is total agreement among the coders k will be NUM if there is no agreement other than chance agreement k will be NUM
here h has some choices as regards the exact position where to drill so s constrains him by saying be careful not to drill through the pattern line
vp v vp head v head vp subcat v subcat
we propose a general algorithmic method of compilation that avoids manual specification
it allows its users to specify a procedure to be expressed in instructional form and in particular allows them to specify actions which must be prevented at the appropriate points in the procedure
the results of this analysis therefore demonstrate that the intentionality and awareness features do co vary with grammatical form and in particular support a form of the hypothesis put forward in section NUM
this often happens when s expects h to be aware that is an alternative to the h should perform and to consider them equivalent while s knows that this is not the case
the efficiency can be further increased by partitioning the linking relation according to lexical and phrasal categories for the left corner and among the lexical left corners open and closed lexical classes
on the other hand the two feature structures are unifiable but their unification produces a cyclic feature structure which raises additional problems for the definition of linking and possibly for implementation
s np vp np agr vp agr
we briefly address these areas separately
in this paper we are concerned especially with morphosyntactic information and illustrate the relevance of predictive linking for morphological analysis and for the analysis of unknown or new lexical items
other problems arise in grammars with indirect left recursion
in this way a tts becomes a particular kind of term rewriting system
tts s are closely related to a class of graph rewr iting
then rule r is applied to the nodes of the second set
the state reached by m upon reading n is recursively specified as
all the above data structures are updated by a procedure called update
we then enter a main loop and retrieve elements from the heap
finally the main loop is exited when the heap is empty
index NUM is next retrieved from h and node m27 is considered
there are several alternative ways in which one could see transformation based rewriting systems
we conclude therefore that dedina and nusbaum s reported error rate of NUM is unattainable
this work suggests several useful ways in which tile perlk rmance of pba systems might be improved
the only relevant difference is that the webster s database is antomatically aligned in their work and hand aligned in ours
in both cases letters and phonemes have previously been hand aligned for the purposes of training backpropagation networks
the threshold used was e times the maximum product score found so far with set by at NUM NUM
because of the shortcomings which emerge in this work we believe the problem lies with pronounce rather than our reimplementation
as a psychological theory pba is under specified offering little meaningfifl guidance on the implementation choices which confront the programmer
for all paths corresponding to the same pronunciation these wdues are summed to give an overall score for that pronunciation
until the recent advent of computational pba models analogy theory could only be considered seriously underspecilied
these are computed using method NUM a priori version of sullivan and damper NUM pp
we apply mdi to the problem of estimating a model consisting of a pair of partitions as described above
open class genotypes contain all other genotypes such as nfs vls v2s v3s
there are several ways lexical probabilities could be estimated for a given language each of them presenting problems NUM
for example the word marine shown in table NUM can have as many as eight morphological analyses
abstract hence language independent the question arises whether the information added during the expansion phase is not language specific to a large extent
this does uot only lead to a variety of different protocols between components which is natural to a certain degree due to the different tasks performed by the components and the purpose of the internee data but also to a number of ditf rent implementation strategies or interfaces
we do not state that agent architectures NUM rph e channels of csp and occam both use rendezvous synchronization
to give a concrete exmnple consider the interface between a speech recognizer and a synt mtic parser
the ore components are used to transform the input data into the output data e.g.
additionally a separate layer idl is present to allow the exchange of more compex data types
this mapping need not be bijective since we allow multiple componen6s within one process see below
this core flmctional model was slightly modified to satisfy the needs emerging from ext eriences with actual systems
analogously a component should detach itself from the ils by sending an appropriate message before leaving the application
the simplest way to guarantee tractability of the disambiguation problem is by restricted computations
the statistical fst is created from NUM gram NUM gram and NUM gram genotype data obtained empirically from the training corpus
since this part of our conceptual knowledge can be formalized as in NUM
thus the kind of reasoning which is involved ill lexieal disambigualion has to be
at the opposite extreme each rule chunk consists of a single rule application this yields a specialized grammar identical to the original one
values on each criterion selection of pieces of information are derived from training corpora by maximum likelihood estimation followed by smoothing
we find this an exciting and significant result and are further continuing our research in this area during the coming year
finally a pruning threshold is calculated as the score of the best path through the chart multiplied by a certain fraction
unless large effort money and time are devoted to this project only small corpora can be disambiguated manually
taking into account the large number of parts of speech the tagger disambiguates correctly about NUM of unrestricted text
an approach that uses hierarchical knowledge is that of resnik NUM which additionally uses the information content of each concept gathered from corpora
evaluation of the results figure NUM shows that overall coverage over polyscmous nonns increases significantly with the window size without losing precision
in a similar approach sussna NUM employs the notion of conceptual distance between network nodes in order to improve precision during document indexing
conceptual density will yield the highest density for lhe subhierarchy containing more senses of lhose rehttive to the total amount of senses in the subhierarchy
then the program computes the conceptual density of each concept in wordnet according to the senses it contains in its subhierarchy step NUM
for our experiments these texts play both the rol e of input files without semantic tags and tagged test files
the results were obtained automatically comparing the tags in semcor with those computed by the algorithm which would allow the comparison with other disambiguation methods
although each of these techniques looks promising for disambiguation either they have been only applied to a small number of words a few sentences or not in a public domain corpus
the method relies on the use oil the wide coverage noun taxonomy of wordnet and the notion of conceptual distance among concepts captured by a conceptual density formula developed for this purpose
the input stream for the shallow parser consists of a double linked list of all extracted fragments found in some input text all punctuation tokens and text tokens like newline or paragraph and all found anchors i.e. all other tokens of the input text are ignored
for example the following fst extracts all genitive nps found in the input stream and collects them in a list tion of extracted fragments is performed by a lexical driven bidirectional shallow parser which operates on fragment combination patterns fcp which are attached to lexical entries mainly verbs
null control parameters in order to obtain flexible control mechanisms for the matching phase it is possible to specify whether an exact match is requested or whether an fst should already succeed when the recognition part matches a prefix of the input string or suffix respectively
layout language preliminary experiments in assigning logical structure to table cells
anchor and introduces two sets of constraints which are used to define restrictions on the type and number of necessary and optional fragments e.g. the first constraint says that exactly one np fragment expressed by the lower and upper bound in NUM NUM in nominative case must be collected where the second constraint says that at most two optional fragments of type tmp can be collected
however it takes into consideration the whole task structure and looks at the realisation of semantic elements as found in the knowledge base instead of two semantic relations not explicitly present in the underlying semantic model
these elements which are the procedural representation of the user s tasks constitute a layer of control which mediates between genre and text but which without genre can not control the grammar adequately
in martin s view genre is defined as a staged goal oriented social process realized through register the context of situation which in turn is realized in language to achieve the goals of a text
the results of this step is shown in figure NUM sub step and goal are found in all three genres while constraint result and function occur in both ready reference and elaboration but are absent from procedure
procedure employs mostly independent clauses and when clause complexes are used the conjunctions are mostly purpose linking a user goal and an action and alternative linking two user actions or two goals
then we will describe the bottom up pattern application based on chart parsing
first all the words in this sequence are assigned the following parts of speech
variables in tile source language expression must be separated by constituent boundaries
only the best substructure can be retained and combined with other arcs
the head words within variable bindings serve as input for distance calculations
an input for distance calculation consists of head words in variable parts
the system is written in lisp and runs on a unix machine
NUM then may i have your credit card number please
there is no flmctional surface word that divides the expression into two constituents
in this paper we will first outline our new translation strategy
this is schematicmly represented as follows null cl b c2 b c1 c2 controllers of sloppy variable yp ilere xp is the anl ecedent for some preform xp and yp is the sloppy variable i.e. a proform embedded within xp
this ability of detecting lexical options may help to perform rephrasing operations
amongst the latter systems the degree of flexibility supported by the text generation component varies considerably
delivers either a description of a single user selected animal or a comparison between two user selected animals
however the user samples they will always end up with the important content
figure NUM dimensions of flexibility in existing systems representation granularity and lin
in this paper we will make reference to the five systems listed below
clearly there are many different ways of introducing flexibility into a hypertext system
section NUM then traws some conclusions concerning the real potential of dynamic hypertext
nonetheless hypertext is attractive because users value their freedom to sample as they choose
figure NUM part of the curator of oz transcripts figure NUM a sample hyper tour
dynamic hypertext is hypertext which is automatically generated at the point of need
essentially their analysis oncludes tie satne when judging string isomorphisnls it is easier to make the judgment of identic flly ordered pairs than it is to reversely ordered pairs
edges in the chart are marked with a category some nonterminal or preterminal symbol from the grammar constituents subs ring span and expectations along with a unique identifier for each edge
similarly concrete indicates the not wrong senses of right the not long senses of short and the gentle subset of the not heavy senses of light
these departures include insertion of adverbial modifiers substitution of other words besides the antonyms or use of the antonyms to modify noun phrases having the same head noun in phrases that otherwise differ from one another
evidently the locational sense of side of is so powerful a semantic indicator for the directional sense of right that people do not use right side of in other senses without providing substantial countervaling evidence for the sense
for each of these adjective noun pairs we determined the number of instances involving each sense we want to determine the extent to which a particular adjective noun pair tends to occur with only one of the two senses
in many of the directional sense uses we find no overt clue within the sentence or the immediate discourse to determine the sense it appears to be simply the assumed interpretation when no specific information contradicts it
we know the structure of the first half of the string and the second half of tile string but not the structure of tile second half the grammar for w could be ambiguous although we can assume that the second w was licensed by exactly the same tree structure as the first
scan i tries to combine inactive charts with the symbol si l at position i
these candidate patterns can be arranged and associated with the chart in the complete procedure
although incremental syntactic formulation is an important issue we do not address this here
some restrictions on patterns must be imposed to avoid infinitely many ambiguities and arbitrarily long translations
the mean processing time on the microvax NUM computer was NUM NUM seconds for an initial sentence NUM NUM seconds per word and NUM NUM seconds for a distorted one
in case of overlapping fragments the problem of choosing among several homonyms could arise but actually overlapping does not occur
in terms of constituents connectedness of a segment would mean that it can be parsed as a single constituent
the system called below corrector uses a formal description of the russian syntax in terms of dependency structures
the paradigm of a typical verb contains NUM finite forms and NUM participles as well as infinitives and certain other forms
now to make the corrector applicable to real texts it is sufficient to supply it with a large morphological dictionary
on the other hand for massively distorted and not too short sentences probability of good results is rather low
for each value of r the degree of disconnectedness c c r is calculated for the results of parsing
it contains only positive rules that describe correct syntss and their parts and are assumed to be used in ordinary parsing
if at least one complete synts has been built then c NUM l otherwise c NUM i
the words before the first word are skipped and also stored everything that comes after the tagged proper noun is resubmitted
if the program itself is certain that a proper noun is found then it tags it and goes on to a next match
the documents returned by the latest run command are filtered and only those satisfying the query are redisplayed
figure NUM shows marked nodes that h ve high similarity
the method proposed here is rela ted to two topics in the litera ture
the area can be efficiently estimate by extracting viewpoints
the similarity is ca lculated according to the rollowing formula
simihrr returns the words similar to w
supcvordi al e returns the superordinate words of the word w
br an unkuown word the possibility of the existence of viewpoints will increase
designers should consider whether the applica tion
requires of context a na lysis
the sequence in which the inputs are
a multi agent a rchitecture beta use
users really need these levels or not
stage NUM is designed to identify these concept nodes automatically under the assumption that most of them will have high relevancy rates
in other words if we sort the concept nodes by relevancy rates then the domain specific patterns should float to the top
the new system autoslog ts can generate a conceptual dictionary of extraction patterns for a domain from a preclassified text corpus
we propose that conceptual patterns for information extraction can be acquired automatically using only a preclassified training corpus and no text annotations
in this case autoslog ts would not propose the pattern murder by x even though it appears in the text
NUM one of the problems with manual filtering is that it is difficult for a person to know whether a pattern will occur frequently or infrequently in future texts
NUM the relevancy signatures algorithm essentially identifies concept nodes that have a high relevancy rate and uses them to classify new texts
to extract terrorists autoslog ts uses a pp attachment algorithm which should attach the pp to the noun murder
this implies that roughly half of the concept nodes in the autoslog dictionary occurred infrequently and probably had little impact on the overall performance of the information extraction system
the autoslog ts dictionary contains NUM NUM unique patterns of which NUM intersect with the autoslog dictionary33 we experimented with automatic filtering techniques based on two criteria frequency and relevancy
french has a rich morphological system for verbs which can have as many as NUM inflected forms and a less rich inflectional system for nouns and adjectives the latter varying in gender and number having up to four different forms
at the same time if we collapse the homographs these NUM morphologically distinct forms get reduced to NUM homographically distinct forms and the ambiguity lies in the NUM forms which overlap across internal verb categories but also across nouns and adjectives
overall for all possible left and right contexts of jmp nmp the guess based on both the genotype and the single left or right contexts will be correct NUM times out of NUM or NUM NUM
there are some problems though due to the fact that there are always words that are overseen therefore improperly tagged or there is disagreement between humans on at least NUM of the words and cross checking by another human is required
this approach is often used for smoothing probabilities but considering the high ambiguity of some french suffixes such as e es etc it is doubtful that basing the estimates on the suffixes alone would give good results
the notation on all arcs in the fst is the following input string output string cost e.g. p hi p NUM NUM the input is a genotype n gram the output represents a possible tag n gram with the corresponding cost
figure NUM example of an fst that tags the genotype bigram p r jmp nmp through one bigram transition shown in bold face in table NUM or through two adjacent unigram transitions shown in bold face in table NUM
the same word marine inflected in all forms of the three syntactic categories adjective noun and verb would have NUM morphologically distinct forms i.e. NUM for the adjective NUM for each of the nouns and NUM for the verb
you want to go at three o clock
the area in the grammar encoding this is key
figure NUM speech functions the semantic stratum
vv rel catc d this procoss t cn lim s a nd calculated tlm tvcragc l crl lexity
we forcibly attached bot h prep no unl and prep2 noun2 to verb on these NUM examples since the two slots prept and prep are judged to be dependent
a heuristic word based method for disambiguation in which the slots arc assumed to be 2a representation of a probability distribution is usually called a probability model or simply a model
since the autoslog ts dictionary has been prefiltered for both frequency and relevancy many concept nodes that represent uncommon phrases or general expressions have already been removed
furthermore the entire text classification system is constructed automatically using only a preclassified training corpus and no text annotations or manual filtering of any kind
self repair occurs moyori no eki made is replaced by kichijoji made
experimental results on spontaneous speech show that this parser stands for a robust alternative to standard ones
we describe thus a microsemantic parser which is uniquely based on an associative network of semantic priming
we start with some additional notation
p of q is indicated by underlying its label
we use an additional set chain i
this results in a deterministic linear time parser
if this word was not primed it is pushed it in a back priming stack
we expect furthermore the insertion of word order constraints to noticeably decrease the perplexity of the microsemantic analyzer
this deterioration is particularly patent for sentences which include at least eight lexemes table NUM
since the parser ignores most word order considerations the interrogative utterances are processed like any declarative ones
the top level contains one cell for each category
underparsing operations are not matched with position grammar productions
thus the faithfulness constraints must ban overparsing cycles
the operations themselves are discussed in section NUM
a position structure has as terminals structural positions
this problem can be avoided in several ways
the first level contains the cells which only cover one input segment the number of cells in this level will he the number of input segments multiplied by the number of cell categories
the candidate descriptions of an input consist of a sequence of pieces each of which has a peak p surrounded by one or more pairs of margin positions m
the cells of the next diagonal are then filled
a block has one cell for each cell category
this is equivalent to mininfizing the data description length as defined in section NUM i.e. i NUM arg minp s log p x
on the other hand since training data
the tagger for messy details has been integrated into the german grammar and has been adapted for the following patterns NUM quantities a cardinal number followed by an amount and a currency name e.g.
the feature val represents the original input e.g. ffinfzig milliarden dollar and the variable string represents the output string of the tagged input in this case fuenfzig milharden dollar
if q1 is the finguistic output structure of the analysis then q is the output structure of refinenlent if q1 subsumes q2 i.e. every local tree in q and every lexical entry in q is subsumed by a corresponding local tree and a corresponding lexical entry in q1
the output of these processes consists in the tagging of the recognized elements p for paragraphs s for sentences w for words in case of morphological analysis the tag m is provided for morphemes and pt for punctuation signs as exelnplified in NUM
the bottom line of text presents a flllly expanded default at each point in the derivation
but a and n are of a complex and unexpected nature a name er dortmund er schweiz er r figure a NUM j pshrig ffinfj hrig NUM prozentig n ex ddr monopolisten hyphenated com null pounding including names and abbreviations
the interpretation of the full tree is that it represents the conjunction of all such mappings for rules NUM NUM n c corresponds to c NUM given condition NUM pl and c corresponds to c given condition NUM p2 and c corresponds to c given condition p
the wfst for a single leaf l is thus defined as follows where ct is the input symbol for the entire tree el is the output expression defined at l t95 represents the path traversed from the root node to l p is an individual branch on 4one can thus define intersection for transducers analogously with intersection for acceptors
this lands us at terminal node NUM the tree in figure NUM shows us that in the training data NUM out of NUM occurrences of aa in this environment were realized as ao or in other words that we can estimate the probability of aa being realized as ao in this environment as NUM
speech and visual data configurations for each experiment
the key requirements on the kind of decision trees that we can compile into wfsts are NUM the predictions at the leaf nodes specify how to rewrite a particular symbol in an input string and NUM the decisions at each node are stateable as regular expressions over the input string
note that as per convention superscript denotes one or more instances of an expression
this is NUM as many arcs and NUM as many states as the transducer for ah in table NUM
this is a sort of lexicalized context free model
we conducted three experiments to evaluate lexical accommodation in various interpretation settings
corresponding to just two out of an infinity of possible paths
next we describe the vpe res system examining the syntactic filter the preference factors and the post filter
the weights are modified as follows the first vp weight is set to be the recency factor NUM NUM
this vp is modified by the comparative clause containing the vpe and thus is correctly selected by the system
note that such vps are parsed as containing the vpe but they are not removed by the syntactic filter
there is an additional penalty for a vp antecedent with a be form auxiliary if the vpe is a do form
however the nearer antecedent is is a be form and is thus subject to an additional penalty
if the selected vp contains the vpe in an argument or adjunct that argument or adjunct must be eliminated
this would not be considered correct according to head match since the head of the coder selection is agreed
one coder selected comes immediately to mind in this connection while the other coder made the same selection as vpe res
the reviewer suggests that examples like this should not be categorically excluded although they are perhaps less than fully acceptable
the recognition task can be broken down into delimitation and classification
table NUM summarizes performance results and compares them to other work
only a few new features allows for significant performance improvement
the generated tree is applied to test data and scored
the pos tagger brill NUM farwell et
the rest can be generated automatically through machine learning techniques
during the delimit step proper name boundaries are identified
other features are derived through statistical measures and hand analysis
parse accuracies for dop1 for different maximum subtree depths
content words that have a close syntactic relation to one another are useful candidates for examination and are intuitively more likely to bear a close semantic relation than words that are near one another but are not related syntactically
disambiguation may be pursued relative to many distinct issues e.g. grammatical class functional role document topic or lexical translation equivalent the entities to be discriminated are the effective senses being identified
in order to construct the alternative sets a small knowledge base is used to determine the semantic type agent object or event of the entities in the discourse model
figure NUM given new status in different sentence positions
within a discourse segment and are oft an
i ought a new book on linguistics
or they can continue ta lking about lh sam liscors o i tot it as iu NUM NUM
in this paper i discuss machine translation mt of english text into 2hrkish and concentrate on how to generate the appropriate word order in the target language based on contextual information
the rest of the sentence forms the ground
our model will assign a probability p w t to every sentence w with every possible binary branching parse t and every possible headword annotation for every constituent of t let w be a sentence of length i words to which we have prepended s and appended s so that wo s and wl l s
a few provisions need to be taken p null wktk NUM if t lcb NUM rcb s ensures that s is adjoined in the last step of the parsing process p adjoin right wktk NUM if h NUM s ensures that the headword of a complete parse is is NUM os t
it can be brought to the full power of spatter by changing the action of the adjoin operation so that it takes into account the terminal nonterminal labels of the constituent proposed by adjoin and it also predicts the nonterminal label of the newly created constituent predictor will now predict the next word along with its pos tag
the model will operate by means of two modules predictor predicts the next word wk l given the word parse k prefix and then passes control to the parser parser grows the already existing binary branching structure by repeatedly generating the transitions adjoin left or adjoin right until it passes control to the predictor by taking a null transition
t k e lcb adjoin left adjoin right rcb i nk null i nk our model is based on two probabilities p wk wk ltk NUM NUM p t wk wk ltk NUM t t l NUM
the principles that guided this propossal were the model will develop syntactic knowledge as a built in feature it will assign a probability to every joint sequence of words binary parse structure the model should operate in a left to right manner so that it would be possible to decode word lat null tices provided by an automatic speech recognizer
let wk be the word k prefix w0 wk of the sentence and wkt the word parse k prefix
a simple alternative to this degenerate approach would be to build a model which predicts the next word based on the preceding p NUM exposed headwords and n NUM words in the history thus making the following equivalence classification
multilinguality and reversibility in computational semantic lexicons
for the latter having a reversed lexicon available is extremely helpful
we claim that we should take advantage of the existing large scale analysis lexicons and use tliem as the starting point in the process of building large scale generation lexicons by first reversing tliem and then enhancing them
this is part of lex ical choice as one can choose to realize the synthetic version or the analytic version of the ee as exemplified in i and ii respectively cf
null statistical very attractive for nlp applications as it seems to replace knowledge based approaches and therefore supplant the needs for human acquisition of large scale semantic lexicons which is a very time consuming task
we give below the example of the partial entry cornpafii a in spanish with two different marmings represented by the following concepts in our world model or ontology cor poration inteirtact socially
the reversed lexicon the algorithm to reverse the analysis lexicon al to produce the generation lexicon gl mainly involves rearranging modifying deleting and adding certain items
figure l sense entries for the spanish lexical item
it seems to depend on the type of generator
an effect of an a tion s component action also holds in the same way
the clthcts of an action are the propositions tlmt hold after the action s successfltl execution
the method has been implemented in prolog
viewpoints make it po ssible to realize t dynamic interpretation of dislance
there may be many such actions
lcb kogure shimazu nakano atom brl
NUM NUM calculating effects and preconditions time map management
figure NUM preconditions of complex action
criterion NUM the a ver ge det l h of the nodes
thus the lcg analysis correctly predicts NUM to be ungrammatical as shown in figure NUM
grew npvap conj npvap co vp ap npvap figure NUM a blocked lcg analysis of the ungram
for example the np kim might be assigned by the lexicon to the category np sg NUM the verb sleeps to the category s npnsg NUM and the verb slept which does not impose person or number features on its subject to the category s np
the constraints value is a set of constraints on variable instantiation
to see this note that because c and c fix the same set of formulae each condition holds iff c and c are elementarily equivalent i.e. for each feature constraint x c x iff c x however the role of partial agreement feature specifications in the two systems is very different
by assigning the nps mh nner and kindern the fully specified case features shown above and frauen the underspecified case feature obj both the feature structure generalization and subsumption accounts of coordination fail to generate the ungrammatical 5a and hb and correctly accept 5c as shown in figure NUM
NUM if the communicative goals are n t well identified it is very easy to convey the wrong impres NUM sion to the reader
this paper describes a system called postgraphe which generates a report integrating graphics and text from a single set of writer s intentions
we would have prefered to use a readily available graphical tool for realization and spend more time on higher level aspects such as the medium selection
ks for the text realization tool we chose to adapt a systemic based text generator called pr texte NUM
these factors include the writer s goals the types and values of the variables to be presented and the relations between these variables
accordingly they write extant analogy models are not capable el predicting the ot tconte of assembly operations for all possiblc strings
the icxical datalmsc consists o1 approximately NUM NUM words based on webster s tbcket dictionary in which text and phonemes have been autodeg matically aligned
since manual alignment generally produces a better result than automatic alignment we ought to produce an even lower error rate than dedina and nusbaum s claimed NUM
also the type of pronunciation lattice used by sullivan and damper in which nodes correspond to thejuncturcs between symbols is likely to be superior
with respect to the semantic interpretation each of source have goal have and locat have just means is suitable as first ar qument in a iiave proposition
the thematic roles and the corresponding grammatical realizations result from the derivation presented in section NUM pre changi NUM sign delivers the first part of verschenken s presupposition
act p e2 e2 a etinw e2 etime e NUM ctmm e2
the number of arguinents as well as the decision whether the arguments are elementary or propositional both depend on tim predicate that directly takes these arguments
in general elementary arguments are represented by variables that have to be filled in by phrases which denote reference objects participants of a situation
discourse rept sentation theory drt is first and foremost a theory about discourse interpretation i.e. it is essentially textually oriented in natm e
thereby the forlner circumstances of disposal sl that result fl om the ciiange si in temp evenl itself are supposed to abut on the return event c
second we have proposed that and described how joined representations an be constructed by e xploiting tile merits of bo h theories
in comparison to these approaches we use similarity information throughout training and not merely for estimating cooccurrence statistics
most previous works define word similarity based on cooccurrence information and hence face a severe problem of sparse data
context words that are far from the target word are less indicative than nearby ones
NUM in contradiction to upper bound of NUM on the similarity values
table NUM the drug experiment the nearest neighbors of the highest weight words
moreover the resulting clusters did not necessarily correspond to the desired sense distinctions
iterations with each fi e we obtain sim y y e
NUM iterations oth according to this stopping condition the algorithm terminates after at most erwise in
note that the similarity is contextual and is affected by the polysemous target word
after that the disambiguation results tend not to change significantly although the similarity values may continue to increase
tags can influence each other over a long distance via transition probabilities
NUM NUM s nl NUM s type sec
cnmputer support is thus needed to reduce the cost of dictionary building
however it is not easy to obtain a very large corpus
it extracts a co occurrence set for each word from a japanese text
we have developed a new method for extracting word correspondences from bilingual corpora
combining them would surely increase the recall and precision for compound word correspondences
furthermore it can not extract more than one correspondence for a word
the precision is the proportion of extracted word correspondences that arc actually correct
b japanese based approximate calculation fig NUM approximate calculation of correlation
finally the pairs of words with the highest correlations are output selectively
in particular the constraints should prevent overparsing from adding an entire overparsed non terminal more than once to the same partial description while passing through the overparsing operations
this seems unreasonably low given that sales of is a strong collocation
question NUM is there a verb between the hjth word and the jth word
for no lexical information all estimates are based on pos tags alone
NUM is the basic model NUM is the basic model
table NUM shows the trade off between speed and accuracy as the beam is narrowed
for no distance measure the distance measure is question NUM alone i.e.
figure NUM a number of clusters versus data size and b kl distance wersus data size
table NUM shows the results of our pp attachment disambiguation experiment in terms of coverage and accuracy
constructed thesaurus is more domaiu dependent and captures the domain dependent features better and thus using it achieves high accuracy
her energy was tremendous her scruples hard to find
the mdl principle is a well motivated and theoretically sound principle for data compression and estimation in information theory and statistics
probability distribution tllat generates such data we assume that the target model can be defined in the following way
since the model description length lmod is the same for each model in practice we only need to calculate and
not all of tile ilouii c ltsters however seem to be meaningful in the useflll sense
the other NUM cases are both for old family doctor
the results are presented under all nouns in table NUM
an adjective and its antonym refer to opposed values of the same attribute
according to mdl the model which minimizes the sum total of the two types of description lengths should be selected
thus there is a trade off relationship between the simplicity of a model and the goodness of fit to the data
the systematic errors to which all automatic parsers are subject
NUM NUM specificity of nouns for adjective senses in the disambiguated subcorpora
the fourth lm investigated was the 20k wsj lm that is available from the abbot ftp site at cambridge university
note also that we do not require any matching of the left siblings when we match the root of a tree in lhs r items iii and iv
a signaling game consists of sender s s sending a message or a signal to receiver r and r s doing some action in response to that message
even if r were unsincere and misunderstood s s message on purpose NUM the nonnatural meaning is still properly conveyed because otherwise the intended misunderstanding would be impossible
that is it may affect the cost of retrieving or composing various content message pairs thus biasing the scope of the game towards those content message pairs closely associated with m
NUM is common in all the version of centering theory but of course there are further details of the theory which vary from one version to another
meaning game is proposed to formalize the core of intended communication in which the sender sends a message and the receiver attempts to infer its meaning intended by the sender
it is a special case of signaling game where us and ur do not depend on the message that is composing sending and receiving interpreting message are free of cost
here the man and he in u2 are more readily interpreted as fred and max respectively which violates NUM and hence our game theoretic account
that is the particular usefulness of abduction in natural language communication could be ascribed to the fact that language use in essence is a collaborative interaction such as discussed so far
we have so far assumed implicitly that s and r have common knowledge about the rule of the game that is p us and ur
however common knowledge on the game might be harder to obtain in natural language meaning games because the game lacks such stability of the typical signaling games as mentioned above
the task of the semantic analyser is to identify this conceptual link
the most specific model is the predicate definition in the semantic lexicon
questionnaire the gold standard being given by health are professionals
this same model constitutes the resource which enables the analyser to handle metonymies
the approach presented here shows how this can be performed
this prol lein does not occur in our approach
the resulting conceptual representation joins the two corresponding paths
this approach to a concel tbased multi role qualia structure
based on this discussion we arrive at the two level model of spoken language phenomena shown in figure NUM the speaker s intention of what to say intended propositional content is combined with the speaker s intention of how to say it pragmatic utterance strategies to form the intended utterance which contains natural speech properties
ana its aol f NUM cni ialte ilel access devic o NUM i NUM
evaluation of the results of our in tplementation demonstrates that accurate anaphora resolution can be realized within natural language processing fl ameworks which do not r can not employ robust and rcqiable parsing components
the overall success of the algorithm is important then not only for the immediate utility of the particular modifications but also because the strategy we have developed for circumventing the need for full syntactic analysis is applicable to other interpretation tasks which like the problem of anaphora resolution lie in the space of higher level semantic and discourse analysis
third close analysis of the most common types of error our algorithm currently makes reveals two specific configurations in the input which confuse the procedure and contribute to the error rate gender mismatch NUM of errors and certain long range contextttal stylistic phenomena best exemplified by text containing quoted passages in line NUM of errors
first with the incorporation of more explicit morphological and contextual information it should 3the set of NUM anaphoric pronouns excluded NUM occurrences of expletive it not identified by the expletive patterns prhnarily occurrences in object position as well as NUM occurrences of it which referred to a vp or propositional constituent
since the sentence in which the anaphor occurs does not contain any candidates the discourse referent introduced by dimensions ix eliminated from consideration by both the morphok gical anct disjoint reference filters only those from the previous sentence are considered each is compatible with the morphological requirements of the anaphor
we are currentlyworking towards this goal see kennedy and boguraev NUM for discussion
case NUM if nl e q then we know that q also matches some portion of c that overlaps with s at the node matched by the periodic node p of q
applying the changes to the psemspec results in x nondirected action lax fill el inclusive b actor a destination c from which penman produces the tank was filled with the water
we can not solve the question of holisticness either but we want to point to the fact that the two verb configurations correlate with a change in aktionsart sally sprayed paint onto the wall is durative she can do it for two hours whereas sally sprayed the wall with paint is transformative she can do it in two hours
the alternation adds a causer to the denotation makes the former actor the new actee and accordingly changes the overall um type from nondirected action to directed action because there is now an actee present
this has the effect of lowering the constant for ordinals thereby is the probability that it will choose a mode other than what is currently prefered
figure i a screen from the duke programming tu tor there is an error at the fifth character in this line
typical dialogues involve repeatedly opening such segments pursuing one subgoal jumping to another returning to a previous subgoal and so forth until the highest level goal is achieved or abandoned
if the match does not exceed a specified threshold the system could look for matches on other recent subdialogues to determine whether the user is attempting to move to another subject
the duke programming tutor allows students in the introductory computer science course to write and debug simple programs communicating with the system using voice text and selection with the mouse
the ordinary prolog depth first search is not used and instead control can pass from subgoal to subgoal to match the segmental behavior that is normal for such dialogues
the output generation module takes this message as input and generates a response based on this information choosing the mode randomly from what is allowed in the message
this allows the algorithm to periodically test modes that it might otherwise avoid and explains why the system used ordinals for the seventh response despite the higher constant
tile representations of tiles two levels are trees
analysis for the other types of manual sentences like definitions
his approach utilizes honorific expressions and the speaker s point of view
table NUM distribution of use of reba
the empirical result supports the our estimation
this is the case of requests
our expectation is summarized in table NUM
therefore we have the following constraint
zero pronouns and conditionals in japanese instruction manuals
there was no incentive to extend lexical accommodation
because this is essentially an information giving and receiving task we expect that the receiver of the information will accommodate to the giver adopting the lexical items used by the speaker who imparts information
the algorithm assigns a noun both an specific sense and a file label
we have experimented willl several fornmlas that follow the four criteria presented above
in order to extend any generation system to an ot parsing system two level optimality theory should be a critical component since it moves the hidden relationship between input and output out of gen and into eval
for example the context in NUM that is the verb tre to be expects a predicative argument and both categories np and ap are just predicative categories
NUM contpare each candidate focus word or phrase in the sentence containing the tl using subjunct with words or phrases in tit senten e extracted in ste l NUM NUM drop any mori hologieally i hmtical words or i hrases as candidates for the focus and select the remainder as the focus of the fo tsing su junct
in a branching order however a third case also occurs x is as oblique as y they do not precede each other
i his is also consistent with the sl ru NUM ural lll iflexity account since the structur3l comtjexity of
initial results show that targeted concept indexing can be extremely effective however random annotation may in fact cause loss of performance
for example we may have multiple unambiguous occurrences of insider trading while very few of trading case
overall the average precision improved by only NUM however some queries namely those where the indexed concepts were in focus roles benefited dramatically
the lexico semantic pattern matching method allows for capturing of word sequences in text using a simple pattern language that can be compiled into a set of non deterministic finite automata
NUM rapid discource analysis for role determination of semantically significant terms NUM the need for well defined equivalence relation on annotations produced by an extraction system
in the first phase only unambiguous pairs are collected while all longer and potentially structurally ambiguous noun phrases are passed to the second phase
in the second phase the distributional statistics gathered in the first phase are used to predict the strength of alternative two word sub components within long phrases
in these experiments we used actual muc organization and people name spotter from lockheed martin to annotate and index a subset of trec NUM collection
the hard soft query mechanism allows a user to specify either in interactive or batch mode a boolean type query which will restrict documents returned by a vector space model match
the following observations were made NUM different queries require different concepts to be spotted concepts that are universal enough to be important in most domains are hard to find or not discriminating enough
see figure NUM for an example of their use
unfortunately we could not simplify the realization level
units are organized in a parallel inheritance graph
working with individual variables is an interesting approach to the problem of graphics generation as it allows the system to reason on the low level components of graphics and it makes it more efficient
figure NUM combined communicative goals evolu
this manual partitioning of goals is useful to organize goals according to themes e.g. a set of goals to present the data a set of goals to illustrate a trend
many systems based on apt NUM NUM use types to determine structure but specific values are often overlooked and the simultaneous use of types and goals is rare
there are many ways of expressing each variable and the system tries to find a way of expressing them all graphically in the same figure if possible or in a set of related figures
correlation is perceptible in the line graph because the two sets of data can be followed together and evolution can be perceived in the point graph because significant year clusters are marked by different shapes
we use e s t to denote the set of english words that are translations the chinese word created by taking all tokens in c t together
currently we are evaluating robustness extensions of the algorithm that permit words suggested by the language model to be inserted in the output sentence which the original a algorithms permitted
NUM NUM attribute and annotation support in applications that incorporate a tipster compliant document manager the tuit api also supports tipster document attribute and annotation browsing and editing annotation and attribute browsing and editing allows users to show create or delete document attributes annotations and annotation attributes
the only way to look up the data stored in the discourse blackboard is to generate variable substitutions
the diagnostic tool takes a coreference annotated text to be evaluated an answer key assumed to be correct and produces various diagnostics which evaluate system performance
in addition we believe that we have strong maximal noun phrase detection and subject verb object recognition and a pattern matching language well suited to a range of tasks
in another application that provides functionality to order items the same rule may apply to disambiguate the items
it consists of three neural networks
moreover for every type a set of appropriate features is specified so that type inference is possible
many unification formalisms allow feature values to be shared
feature pairs are listed with their corresponding chunk
figure NUM algorithm for converting a parse to a
figure NUM feature structure with the meaning by
the parser produces feature structures holding semantic information
on to the converting algorithm shown in figure NUM
fourth we describe the tmrser s neural aspects
more specifically the functor dtr indicated by the value funct of the attribute subcat shares the value of the attribute selects with the synsem value of the head dtr and its projects value with the syn attribute of the mother
comment this success is surprising in two respects total rwprdseg rliftana refine sol NUM NUM NUM NUM NUM NUM NUM NUM NUM for industrial purposes this may still be too slow but we think that the figures show that the system is not so far away front reality
this is different here in that a corpus based approach to granlmar development has been adopted which is the implementation of the sinlple principle that if a grainnlar is supposed to cover rem texts that the coverage of these texts has to be determined first
in order to give a general tlavor of the corpus investigation one noteworthy result should be reported NUM of the number of words occur in nps of the structure det a a n
though the advantages of ugs are obvious in that properties such as monotonicity declarativity perspicouity are important for maintaining and easily extending grammars their popularity despite NUM years of history is still restricted to the academia
the strata of the linguistic resources are organised into networks of choices each choice resulting in a different meaning realized i.e. expressed by appropriate structures
for those cases where the realisation remains under determined by the task element type we conducted a finer grained analysis by overlaying a genre partition on the undifferentiated data
the intention that the reader should recognize the differences in function of each section is underscored by the use of distinctive typographical devices such as fonts and lay out
no tools exist to automate a functional analysis of text which makes coding a large body of text a timeconsuming task
function is closely related to a goal in that it is also an action that the user may want to perform
surprisingly it appears that large french companies often have their documents authored in english by francophones and subsequently translated into french
our analysis goes beyond previous work by identifying within the discourse context the means for exercising explicit control over a text generator
in this paper we have shown how genre and task structure provide two essential sources of control over the text generation process
that is lexas is only concerned with disambiguating senses of a word in a given pos
we compared the classification accuracy of lexas against the default strategy of picking the most frequent sense
it is harder to dis null ambiguate words coming from such a wide variety of texts
this is partly because there are not many common data sets publicly available for testing wsd programs
we call this method most frequent in table NUM
there are two instantiations of this strategy in our current evaluation
we call this method sense NUM in table NUM
integrating multiple knowledge sources to disambiguate word sense an exemplar based approach
much recent research on the design of natural language lexicons has made use of nonmonotonic inheritance networks as originally developed for general knowledge representation purposes in artificial intelligence
the proper generalization is simply the syntactic construction including its variants in which the non anaphoric it does not occur
the response to such old masters as michelangelo rembrandt and velasquez was and still is instant wonder and delight
furthermore certain departures from the perfect phrasal substitution patterns equally constrain the senses of the antonyms to be concordant
the chief function of these conjoined and prepositional co occurrences is to cover the range of possible values of an attribute
they indicated that no new errors were being made and that all old errors would be corrected within NUM days
katz principled disambiguation involved in some noun based disambiguation and addresses the potential of other types of indicators for adjective senses
in particular the paper assesses the potential of nouns for discriminating among the senses of adjectives that modify them
man therefore can be taken as a fairly good indicator of the aged sense of old
our immediate interest however is in discovering actual patterns of usage and not in building an automatic system
most nouns by far were modified by the target in only one of its senses in our co occurrence sentences
NUM all t tests were unpaired two tailed
we discuss each of these in turn
the reactive planner also uses rstlike operators
third the lexicon must be created
figure NUM the edp application algorithm
these are its content specification nodes
figure NUM example content specification nodes
a representation of embryo sac formation
NUM NUM semantically rich large scale knowledge bases
NUM it is straightforward to add extra datr code so as to derive gender feminine when gender is feminine is true and gender masculine when gender is feminine is false or conversely
with a suitable degree of abstraction achieved by parameterization of the components lexical rules can be reified in a language like datr allowing one to inherit from another
it relies heavily on context as encoded in features which describe the morphological syntactic semantic and other aspects of a given parse state
applying machine learning techniques the system uses parse action examples acquired under supervision to generate a deterministic shift reduce parser in the form of a decision structure
the parsing does not have separate phases for part of speech selection and syntactic and semantic processing but rather integrates all of them into a single parsing phase
agent time this involves pattern matching with corresponding entries in the verb subcategorization table whether or not frarne NUM and frame l satisfy subject verb agreement
since the currently NUM features are supposed to bear some linguistic relevance none of them are unjustifiably remote from the current focus of a parse state
whenever the feature set is modified this step must be repeated but this is unproblematic because this process is both fully automatic and fast
besides word pair entries the bilingual dictionary also contains pairs of phrases and expressions in a format closely resembling traditional paper dictionaries e.g.
consider for example the term interest rate
the NUM blocks are then consecutively used for testing
table NUM translation evaluation results best possi
table NUM comparing our system contex with
that can be considered obviously relevant
tions such as number person and tense
morphological ambiguity is captured within a frame
number of training sentences pr prec
table NUM correlation between various parse and
the lexicon contains about 110k root entries
the pi e system runs quite fast
the out person is similarly defined
c lyn4 races hers on weekendss
table NUM explains some of them
component s static knowledge dynamic structures
assertions that contradict previous assertions are ignored
including people mm es com pa ny
the significance weights are acquired through corpus based training
there are many ways to combine evidence weights
in our experiments we tried the following
ual eviden lcb e and in
tg NUM is being used in the domain of appointment scheduling within dfki s cosma system
a specialized tgl grammar editor could improve the development and the organization of grammars considerably
moreover formal as well as informal opening and closing phrases for emails are covered
it accounts for non local dependencies between substructures such as updates of a discourse memory
gil is the basis for the precondition test predicates and the selector functions of tgl
different sorts of free temporal and local adjuncts can be specified by corresponding features
this contradicts the idea of having the user specify her preferences independent of tg NUM properties
the expanded out lexicon consists solely of lexical entries whereas the covariation lexicon is made up of three different data structures the extended base lexical entries the interaction predicates and the lexical rule predicates
we mentioned in section NUM NUM that eliminating lexical rules in a precompilation step makes it impossible to process lexical rules or lexical entries that impose constraints that can only be properly executed once information from syntactic processing is available
NUM as a result of unifying the out specification of a lexical rule in a path of the finite state automaton with the in specification of the following lexical rule the out specification of the second rule can become more specific
the first compilation step discussed in section NUM NUM translates lexical rules into a definite clause representation and derives for each lexical rule a frame predicate that ensures the transfer of properties that remain unchanged
but table NUM shows the need to make finer distance distinctions than just whether two words are adjacent
the denominator is a normalising factor which ensures
the need of planning that is the need to look ahead and to plan in general terms increases with sentence length and with the ntnnber and type of embeddings for example center eml xtded sentences
thus it is necessary to make several passes through the states attempting additional pruning at each pass until no more improvement is possible
however since the decision tree for each phoneme is learned separately the the technique misses generalizations about the behavior of similar phonemes
number of random variables and the time complexity of the algorithm is of the same order as it is linear in the number of parameters
the x axis is pa and the y axis is pb
treebank part of speech specifications were not used to constrain parses
finally an extension of the analogical speech translation approach is proposed that accounts for such higher level pragmatic information
since sentence NUM is pragmatically neutral the pragmatic information from the original sentence has been lost
when speech performance errors are corrected by the speaker within the utterance they result in slip of the tongue repairs
speech performance errors are obvious errors not intended by the speaker and for our purposes not bearing any information
however strategies to further reduce the imposing effect of these request forms are usually not directly transferable across languages
given a number of independence assumptions the most probable example can be computed efficiently with a dynamic programming algorithm
i gave it to you on monday yeah probably on monday the 27th
many spoken language systems have thus been using robust pattern matching techniques to overcome these problems
we call such repairs elaborating repairs in contrast to slip of the tongue repairs which result from correcting speech performance errors
some types of repairs in which a phrase is paraphrased or repeated with more information also ful
this actually adds u to the current context
texts found for any pos were so sparse that we used a hash algorithm
then it follows that the problem is formalized as an optimization problem minimize
this result tells us that our method is useful as a preprocessor for a tagger
adding extracted words to the dictionary the accuracy of a morphological analyzer augmented considerably
judgement whether the string belongs to pos or not was made by hand
in this formula summation is calculated for the set of poss in consideration
we use as this measure the square of euclidean distance betwen vectors
this result is consistent with the result derived from the hypothesis that we described in section NUM NUM
besides there is a tendency that in proportion as the frequency increases the precision rises
a similar problem has been found in be verb phrases
only considering inflected forms means that we are systematically underestimating frequencies but since the main aim is to acquire the correct relative ordering of lexical rules this is not too problematic
simplified version of verb subcategorization is then encoded as
fig NUM also contains a discourse relation ex
the grammar acquisition scheme described above has not yet been automated but has been manually simulated for a set of NUM english japanese simple sentence pairs designed for use in mt system evaluation which is available from jeida the japan electronic industry development association the japan electronic industry development association NUM including NUM any question will be welcomed
in this approach an o t NUM algorithm similar to the one described later can be constructed to replace a search
b addition of new patterns if there is no such paired derivation sequence add specific patterns if possible for idioms and collocations that are missing in t or add the pair s t to t as a translation pattern
NUM she made him an excellent wife
the consultation process is continuing but i can confirm now that the necessary funds will be made available to meet the key targets
in contrast the new algorithm generally takes less than one minute usually substantially less with no special optimization of the code
in order to be able to rank interpretations in this way it is necessary to construct a parser which operates stochastically not deterministically
while resnik s method is based on an interesting intuition the justification of this method from the viewpoint of statistics is still not clear
composition is always done between two lambda terms one of which at least contains a free variable which gets bound at the time of incorporation
we establish a list of acceptable type sensitive composition rules which tell us how to compose two flmctions according to their types
probabilities of different word senses can be learned by a running analyzer to the extent that lexical ambiguities are resolved either during processing or by an external oracle and for limited domains this may well be the best approach
our method selects optimal word classes according to the distribution of given data and smoothes the three word probabilities using the selected classes
finally statistics on the performance of the disambiguator are presented
therefore heavily inflecting languages would tend to produce unambiguous word forms
1c adj or in the whole cohort e.g.
the lexicon has a total of about NUM NUM words
the syntactic mapping of context sensitive word forms is left to the cgp
conclusion the morphologicm anmysis of swmfili tends to produce a comparatively large number of ambiguous readings
in swahili lexicon underspecification was avoided which adds to ambiguity
descriptions of its development phases are found in several publications e.g.
we think that a probabilistic approach is especially attractive because it is able to employ a principled methodology for acquiring the knowledge necessary for disambiguation
for such cases the rule syntax should allow the use of more distantly located information
those are typically in such positions in a sentence that writing of reliable rules is difficult
this implies that i a system builder does not have to create himself the slrnctural knowledge needed to describe the events proper to a sufficiently large class of m afive documents ii it becomes easier to secure the reproduction and the sharing of previous results
in the cobalt project we have then used a commercial product tcs text categorisation system by carnegie group to pre select from a corpus of reuters news stories those concerning in principle the chosen domain financial news about merging acquisitions capital increases etc
by means of proper specialisation operations it is then possible to obtain from the basic templates the specific derived templates that could be concretely needed to implement a particular practical application e.g. move an industrial process and the corresponding occurrences
our contribution has consisted in tile set up of a rigorous algorithmic procedure centred around the two fouowing conceptual tools the use of rules evoked by particular lexical items in the text exmnined and stored in proper conceptual dictionaries which take the form of generalised production rules
predicative templates are characterised by a threefold format where the central piece is a semantic predicate a primitive like behave experience move produce etc whose arguments role fillers are introduced by roles as subj ect obj ect source dest ination etc the data structures proper to the descriptive component are then similar to the case grammar structures
the first is a catalogue giving a complete description of the formal characteristics and the modalities of use of the well formed basic templates like moving a generic object mentioned above associated with the language presently about NUM pertaining mainly to a very general socio economicopolitical context where the m fin characters are human being or social bodies
as we pointed out earlier however if we are to obtain the n most preferred interpretations we need to use syntactic likelihood
this went some way to addressing the technical aut hors desire for a formal model and tools to lmild it building the model dora scratch howe ver even with the help of our menu lmsed interface was a tedious and lengthy process which could potentially have rendered tile i i lcb aftei lcb system impractical
we refer to that word which exhibits the subcategory feature of a category to be that category s head word
rap prefers an interpretation attached to a nearer phrase while alpp prefers interpretations with attachments that are low and in parallel
when training the length probabilities the parameters in NUM may be estimated using the frequences in NUM
for the sentence shown in figure NUM b there are two interpretations alpp would necessarily prefer the former
lpr implies that in natural language one should communicate as relevantly as possible while rap and alpp implies that one should communicate as efficiently as possible
let us consider a simple case in which we are dealing with a modifier category m a head category h and the head word of h w
to cope with this problem we used machine learning techniques recall the merits of using machine learning techniques in disambiguation as described in section NUM
as k is very small in our case k NUM the number of parameters in a length probability model is of n s polynomial order
the lexicon consists of three parts one a syntactic and semantic microfeature vector per word second lexical feature values and three statistical microfeatures
the chunk n label principle has a few theoretical limitations compared with the feature structure formalisms commonly used in unification based parsing e.g.
the parser is trained with transcribed data only but evaluated with transcribed and speech data including speech recognition errors
lexical feature values are stored in look up tables which are accessed when the linguistic feature labeler indicates a lexical feature value
all performance measures are run with transcribed t sentences and with speech s sentences containing speech recognition errors
performance measure NUM is the end to end translation ratio for acceptable nontrivial sentences achieved when lr generators are used as back ends of the parsers
ebr example if the previous sentence consists of three simple sentences tile firs simple sentence in tile previous seiltence becomes tile third from the current sentence after partitioning
if errors are still found the pruning operation is reversed
to determine to what extent we should extend the range of search for the antecedent we make the following investigations and e xperiment how many simple sentences does a naturally occurring sentence consist of
the centering theory specifies the following heuristic rule if the cb of the current sentence is the same as the cb of the previous sentence a zero pronoun should be used
exp1 fin how zero l lt lllis in omp ex sen o 1ic s
discourses this omission o ms iltol c fi eqllelt ly ltd a zclo pr ilollll is of o ll
we think this tendency in plies that noun phrases in the sentence before the conjunctive t ostt ositions of class b tend to be not the antecedents of zero pronouns in the next sentences
state with all of the incoming and outgoing transitions of s and f
the entire training set of transductions is tested after each branch is pruned
our kmnework that identities tit a nt ccd it s of z wtl iii lllolllts in naturally occurring aimnes
most hand classified clusters for kau consist of more than one clusters found by maximizing the association score
in corpus based nlp acquisition of lexical knowledge has become one of the major research topics
the classification process proceeds according to the following steps NUM
sense distribution of english predicates and lapanese ease element nouns
in roget s thesaurus the verb han q
the speech error literature abounds with examples supporting this point of view
however we do not feel it is worth preserving offset or alignment or distortion parameters simply for the sake of preserving the original translation channel model
but how is such a tree built
the word s underlying meaning is not in contradiction with the initial message
having two unspecified elements person event NUM we have to choose
where NUM is the udrs disambiguation and b u the linear logic consequence relation
this is of course the province of lexical mapping theories lmts
we thank our fracas colleagues and anette frank and mary dalrymple for discussion and support
without going into details f works by adding subordination constraints turning partial into total orders
for our present purposes it will be sufficient to assume a lexically specified mapping
given this set of variables the pattern assigned to card can matd the text version of all cardinal numbers from to NUM e.g.
the following algorithm of conditional parallel replacement will consider all empty upper as being of type
conditional parallel replacement denotes a relation which maps a set of n expressions ui i e NUM n in the upper language into a set of corr sponding n expressions li in the lower language if and only if they occur between a left and a right context ll
an iimm transducer builds on the data probability matrices of the underlying hmm
the tag of the first word is selected based on its initial and class probability
notice that rationalised french which is not respected in the procedural texts we have analyzed will assign a higher priority to rl resulting in an identical parameterisation of the lexicalisation mechanisms for both languages
nevertheless the co occurrence sets can be still associated owing to the other correspondences between them that arc contained in the bilingual dictionary
automatic induction of finite state transducers for simple phonological rules
this is the advantage of probabilistic approaches such as the riley withgott approach
in japanese this is not so easy
we also mention some less discussed problems
this may also make bracket recall seem too low
towards a more careful evaluation of broad coverage parsing systems
the expected value and variance of yy would be
NUM double bracket pairs are removed
we also present two types of precision
the items for the regular test are listed here
we give two types of recall
thus we report the values of the k statistics for each feature we coded for
the significance levels for intentionality and awareness indicate that the features do correlate with the forms
in semantics and pragmatics negation has been extensively studied cf
the point we want to emphasise here is a methodological one
a corpus study of negative imperatives in natural language instructions
of the NUM total examples NUM met this criteria
the percentage agreement is NUM NUM for intentionality and NUM NUM for awareness
in the domain of cooking no agent would consciously burn the garlic
discourse segment beginnings in trp is effective
for example if it is known that max never gets angry and that fred is short tempered then both in NUM and NUM the second utterance will preferably be interpreted as meaning angry fred max
so it can be analyzed in game theoretic terms rcb in this paper we study a fundamental aspect of linguistic communication from the point of view of game theory and enumerate some basic issues involved in the communication games of natural language
figure NUM depicts s s inference when she wants to communicate cl where the players have common knowledge of c lcb cl c2 and m lcb ml m2 rcb but not of their utility functions
this restricted sense of nonnatural meaning implies that communication is inherently collaborative because both s and r want that r should recognize c and i s of course wants it and so ers for general reference on game theory
the top branch is the nature s initial choice of s s type according to p the middle layer is s s decision on which message to send and finally the bottom layer is r s choice of her action
the combination s an of strategies is an equilibrium NUM of a signaling game when as and a are the optimal responses to each other that is when az maximizes x s expected
the factors which define a meaning game include grammatical function reference by lightweight message extralinguistic information these affect p grammar cost of recalling these affect the utility and so on
basic issues involved in the game of natural language communication are discussed such as salience grammaticality common sense and common belief together with some demonstration of the feasibility of game theoretic account of language
collocations are perwtsive in language letters are deliw red tea is strong and not powelful we l mt progrants aitd so oll
the algorithm for the extraction of tile candielate collo ations follows e xtract the n grams decide on the lowest frequency of collocations renlove tlle i granls below this frequency lbr all n grams a of lllaxihlulil length
h r all smaller n grams a in descending order if total frequency of a frequency of a in a longer string a is not a collocation else if a appears for the first time
eolnpouilds as xamples of collocations some admit only collocations onsisl ing of pairs of words while others admit only eollo ations consisting of a maximum of tive or six words some emphasize synl aglnat ic aspecl s others selnmtl ic aspects
tim increased inl erest in collocation ext raetion comes from t hu faei l hal t hey can be used for many nlp at plical ions such as machine transla ion maehilw aids r r t ra nslal ion
if a appears as a substring in one or more collocations not with the same frequency then it is assigned i i t NUM where t a is the total frequency of a in longer candidate collocations and c a the number of ttmse candidate collocations
though the examples here are front domain specific lexieal collocations grammatiem ones can be nested as well put down as put down for put down to and put down
third there is no feedback from the linguistic to the conceptual component
step NUM since there are two events the speaker lifts to choose
let us see some of the advantages of our approach
in this respect the initial reordering of rule NUM which led to rule NUM in the final grammar in figure NUM is crucial see section NUM
the figure NUM consist of two tree structures connected through dotted lines of which the left one corresponds to the filtering part of the derivation
the abstract unfolding tree in figure NUM clearly shows why there exists the need for subsumption checking rule NUM in figure NUM produces infinitely many magic vp facts
the resulting processing behavior is similar to the behavior that would result from head corner generation except that the different filtering steps are performed in a bottom up fashion
the presented research was sponsored by teilprojekt b4 from constraints to rules efficient compilation of hpsg grammars of the sonderforschungsbereich NUM of the deutsche forschungsgemeinschaft
more specifically magic generation falls prey to non termination in the face of head recursion i.e. the generation analog of left recursion in parsing
performing this optimization throughout the magic part of the grammar in figure NUM not only leads to a more succinct grammar but brings about a different processing behavior
in the example the seed is used without any delay to apply the base case of the vp procedure thereby jumping over all intermediate chain and non chain rules
if rule r has head say p lcb the modified version is obtained by adding the literal magic p t to the body
figure NUM connecting up facts resulting from semi naive generation of the sentence john buys mary a book with the magic compiled grammar from figure NUM
as to the sentences with only unknown category words the improvement of dop3 with respect to dop2 is most noticeable the accuracy increased from NUM to NUM
the restriction will not hold of subsequent instances of the nonterminal marked for copying in the same local domain nor at ditferent levels in the analysis
vogel is grateful to the sfb NUM for funding his stay stuttgart hahn acknowledges the support of esrc grant no r004293341442 branitan epsi c research studentship no NUM
the idea ix that speakers of bmguages with ww homomorphisms have a different pattenl of invoking copy checking than those who speak lan null guages that do not admit cross serial dependencies
it might be claimed that just as we argue ww not to require the worst case complexity for its language class ps1 NUM
even the relative estimates for productivity of rules will be inaccurate if there is a systematic difference between the frequency of words in one input class as compared to another since infrequently occurring words are less likely to have attested derived forms
practically this has considerable advantages over the earlier proposal that blocking should be detected by looking for synonyms since the the state of the art in acquisition and representation of lexical semantic information makes it difficult to detect synonymy accurately
for word choice in generation it would be appropriate to take the highest probability suitable entry and if none are attested to construct a phrase rather than apply a semi productive lexical rule to produce a nonce form
if we assume a precompiled representation of this form conditional probabilities that a word form will be associated with a particular basic or derived entry can be associated with states in the fsm as illustrated in figure NUM
finally theories of the lexicon in which the consequences of lexical rules are precomputed can not be correct in the limit because of the presence of recursive lexical rules such as re anti or great prefixation e.g.
the derivation probability which gives the probability of a particular sentence interpretation will depend on the product of the lexical probabilities rule probabilities might also play a role but can be ignored in the categorial framework we adopt here
black et al NUM schabes NUM resnik NUM but the difficulty of acquisition means that the validity of utilizing lexical probabilities of the type assumed here has not yet been demonstrated on a large scale
thus a rare word with only a few observations may be more likely to be seen in an alternative realization than a very frequent word which has been observed many times in some subset of the possible realizations licensed by the grammar
such a strategy is compatible with and may well underlie the gricean maxim of manner in that ambiguities in language will be more easily interpretable if there is a tacit agreement not to utilize abnormal or rare means of conveying particular messages
for the purposes of dei iving a productivity measurement for the rule as a whole it does not matter much if the set is incomplete as long as there are no systematic differences in productivity between the included and the excluded cases
they can also mark the fragments which must be retained after the stage even if they are not maximal
there were NUM well formed sentences in the first series of distorted sentences and NUM in the second series
experiments with the corrector showed from the very beginning that the process described often generated redundant hypothetical corrections
the maximum weight over all constructed syntss is counted and only syntss with that weight are retained
correction of errors is reduced to parsiug on the extended morphs as described in ction NUM
in our case due to incompleteness of the grammar many well formed sentences would have c NUM i
the new homonyms arise as the result of varying the forms of the words of the input sentence
the process used to find corrections is quite similar to the ordinary parsing described in the previous section
tile morphs is regarded as input information for the parser which is based on the bottom up principle
wrong corrections were proposed NUM cases gave system failure exhaustion of time or memory quotas
NUM variants of replacement NUM NUM application of context constraints
wouldbe split into the group of empty upper
NUM a upward oriented lcb u1 l1 ii NUM ra rcb NUM lcb u l ii in r
any symbol the universal sigma star language contains mi possible strings of any length including the empty string string beginldng or end see
NUM a is replaced by b only when it is at the beginning of a string or etween v and the two tinal symbols of a string i
replacements of all of these three types directions can be optional i.e. they are either made or not
operator verbs are exceedingly common in the french versions
the different transducers due to their structure and size
that are more skewed to the right figure NUM an outline of the rhetorical parsing algorithm
but that appeared in the corpus more than once are called unknown words l he proper positions of the unknown words in the thesaurus are estimated by using wom to word relationships extracte l from a large sca le
hough stl h features are implicitly used in the creation of most existing thesauruses according to hilriaji in tuition they axe lost wheii the constructe l t le sara uses are used
however in l ositioning a word whose sense is NUM NUM knowtl a suitable position must be selected from thousands of nodes words it the thesaurus and therefore it is very difficult to position the word with pinpoint accuracy
wi l t il w hwehll ittg lhe getiera l uiiclj lcb ins described iii 1jie i r wious se ion to realize a large scale thosatir is t rcb l nij systoills
2f effectuev la purge du circuit d aspiration
step NUM specification design the diflieulty level oft he interface tesig should be lel er l hted
verloj y inult im dal delete speech or keyboard delete
en itiesj but i a ther iiltlst be viewed as events which occur oil a li objeci
step NUM grammar rule writiug each selected mu ti moda expression is defined by the corresponding gra mma r
the major purl ose of this paper is to define a frameworl and esign methodology for a cosnpul illg
for exa nq le saying l e ete this circle while i oinl ittg
a single gnunm r rule in mm i c cain allow the coexistence of gra nnna tica l
as described earlier a corpus of low homogeneity should produce a lm of higher pp than a corpus of high homogeneity
however it is clear that when used on the real spoken data the email lm provides the lowest error rates
what is required is an objective measure that reliably identifies which of the domains in the bnc is most similar to hp email
the table also shows a polarity of the bnc the arts domains at one pole attracting each other e.g.
conversely ff banana is ranked NUM NUM in one corpus and NUM NUM in another this is a very insignificant difference
furthermore domains such as applied science are very coarse grained they contain many more types of material than just those of computing
although the significance of the correlation is not in doubt the differences are highly significant too
in such a case this cutting needs
however these variables are not independant
investigation of the spoken part of the bnc is therefore suggested as an area for further work
deletions substitutions reduce the value of h since h n d s
the computation of the relations gives
NUM derivation grammar and cf parse forest
form of ligs with productions of the form
let n be the length of the input string x
the o relation select pairs of non terminals
find all the linear so a derivations
the examples in section NUM are produced this way
complements another problematic case for the current binding theory comes from portuguese as it fails to make the correct predictions for binding patterns involving reflexives in the context of verbs with two oblique complements
it should be mentioned that only the grammatical realisation NUM component of glose can be considered as an implementation of pure my models since we do not use at the lexicalisation phase mtt style semantic networks which represent in this theory a linguistically motivated semantic level independent of the conceptual level
the rules should be able to introduce modifiers on the main predicative element of the sentence i.e. the main verb in rx and the direct object of the operator verb the predicative noun in r2 in rx an attribute of the action will be realized as an adverb linked to the main verb v by means of an attributive deep syntactic relation attr
n the one hand the su cat list records in brnmtion about strict subcategorization properties of the corresponding predicator through the nature and number of its elements
noun phrases containing descriptions of objects are generated by traversing the feature structure representing the object in depth first order and mapping the features and types to strings
rules ri are depicted in figure NUM we write nij to denote the j th node in a post order enumeration of the nodes of lhs ri NUM i NUM and NUM j NUM therefore n35 denotes the root node of lhs r3 and n22 denotes the first child of the second child of the root node of lhs r
it is important to observe that since some rewriting of the input tree might have occurred in between the time n has been inserted in rule i and the time i is retrieved from h it could be that the current rule ri can no longer be applied at n
q2x e not indicated above NUM q q a qll although the number of states of ac is exponential in in i in practical cases most of these states are never reached by the automaton on an actual input and can therefore be ignored
definition NUM a deterministic tree automaton dta is a NUM tuple m q qo f where q is a finite set of s ates is a finite alphabet qo e q is the initial state f c q is the set of final states and NUM is a transition function mapping q x e into o
the lemma then follows from the specification of set f and the treatment of set n in items iii and iv in definition NUM we also need a function mapping f x lcb NUM r NUM rcb into lcb NUM r rcb u lcb NUM rcb specified as min NUM
even better accuracy can be achieved with a more fine grained link class structure
in this paper we report on smes an information extraction core system for real world german text processing
template generation an fcp expresses restrictions on the set of candidate fragments to be collected by the anchor
if successful the set of found fragments together with the anchor builds up an instantiated template or frame
an fcp also defines which sort of fragments are necessary or optional for building up the whole template
such an vgf is then used as a complex anchor for the selection of appropriate fragment combination patterns as described above
fcps are used for defining linguistically oriented general head modifier construction linguistically based on dependency theory and application specific database entries
using this mechanism it is possible to define or re define the output structure without changing the whole fst
consequently the major goals which were identified during the sixth message understanding conference muc NUM were
however since the form of event announcements is usually not standardized shallow nlp mechanisms are necessary
the capability of efficiently processing compounds is crucial since compounding is a very productive process of the german language
any utterance is divided into sdus semantic dialogue units which are fed to the parser one at a time
although english can conflate all those possible meanings into one expression the translation into other languages usually requires more specificity
the general topic provides some information about what types of exchanges and therefore speech acts can be expected
sdus represent a full concept expression or thought but not necessarily a complete grammati null cal sentence
a secondary evaluation will be ithe final results of this evaluation will be available at the time of the acl conference
janus deals with dialogues restricted to a domain such as scheduling an appointment or making travel arrangements
for any given utterance out of what we can loosely call context there is usually more than one possible interpretation
in example NUM it will be an acceptance because it is uttered after the previous speaker s suggestion
based on the quality of the speech act disambiguation itself regardless of its contribution to translation quality
within the task performance phase very many subdialogues can take place such as intbrmation seeking decision making payment
to make this lailn realizable we must explain two important notions ironic eilvironment and implicit display
9f gonfler l accumulaleur h l azote
the two verbs have distinct argument structures
NUM NUM consequences for the lexicalisation mechanisms NUM
this paper also describes a method for computationally formalizing ironic environment and its implicit communication using situation theory with action theory
our contrastive analysis concentrates on verbal expressions
this structure represents an imperative illocutionary act
table NUM lists the t i agmatic principles violated by the ironic utterances in figure NUM
iof mettre l avion sur vdrins
however in the lcg treatment of agreement proposed here agreement is inherently asymmetric in 1because conjunction and disjunction are the only connectives we permit it does not matter whether we use the classical or intuitionistic propositional calculus here
in the unification based accounts agreement is generally a symmetric relationship between the agreeing constituents both agreeing constituents impose constraints on a shared agreement value and the construction is well formed iff these constraints are consistent
both the feature structure generalization and subsumption accounts incorrectly predict it to be well formed as shown in figure NUM NUM er findet und hilft m nner und he find acc and help dat men acc and
to simplify the presentation of the proofs we formulate our system in natural deduction terms and specify the properties of the boolean connectives using the single inference rule p rather than providing separate rules for each connective
given a set of atomic features NUM we define the set of feature terms NUM and categories g as follows where and v are the standard lcg forward and backward implication operators
an experimental addition of relative clauses to the speech grammar was productive only in the linguistic sense since the resulting exponential increase in grammar size caused recognition rates to drop to unacceptable levels
if the node does pass selection its regularization is augmented with the relevant semantic class and role information becoming an intermediate semantic representation suitable for further processing such as reference resolution and quantifier scoping
thanks to ede zimmerinann an l hans kamp for useflll discussions and to the anonymous reviewers for oinulel ts
deictic reference operates similarly to eucalyptus a mouse click can select a number of overlapping map objects at once to be resolved by an accompanying verbal reference for example what s the population here
a non immersive desktop version of the viewer allowed mouse selection of a platform and thus singular deictic reference this helicopter but the immersive display version did not include a dataglove or other pointing device
for speech input we use the phonetic engine pe200 from speech systems inc with the speech recognition software running on a sun workstation to which the pe200 hardware is connected by a serial line
for speech output a dectalk speech synthesizer is connected to the other sun serial port and can be sent output from nautilus either by unix system calls or by writing data directly to the port
total input coverage is on the order of NUM million utterances deliberately high to test the speech system s ability to detect a wide variety of noun phrase determiners
one of the attributes composed by proteus during parsing is an operator operand regularized form intended to serve as the representation to which semantic selection and interpretation rules can be applied
the eucalyptus lexicon totals about NUM words many of them unused morphological variants generated automatically by the proteus lexical macros by comparison the vocabulary for the speech recognition front end is only NUM words
lace includes a large object oriented cartographic database of most of central germany containing a total of over NUM NUM objects such as towns lakes rivers and railroads
ssince we are primarily interested in the process we abstract fi om furtl er details like temporal aspects
criterion NUM the nund er of viewpoints
basic word adnomlna liza tion e.g.
the i asic idea is very simple
however some relationships ma y be noisy
fig NUM exanlple of viewpoints the thesaurus
go to church histruinent etc e.g.
we have concentrated on the choice NUM of tone as a major signal of interpersonal semantic features such as speech acts and speaker s attitudes
however the choice of mood is crucial since it leads to a whole variety of options that are eventually realized in different tones these are the key systems
tude towards the proposition being expressed surprise reservation what answer is being expected emphasis on the proposition etc referred to as key features
a major constraint on the mapping between speech function and mood is the kind of discourse or genre and the type of discourse stage the message is produced in
also it is uncontroversial to maintain that intonation potentially reflects a speaker s attitude towards the message she verbalizes see e.g. NUM
the novelty of the approach pursued lies in the move away from text to speech and concept to speech generation towards communicative context to speech generation see section NUM and the integration of dialogue representation nl generation and speech synthesis
this is additional information that the system volunteers the user which often take the form of a polite command e.g. bitte warren sie please wait
therefore we also have to consider the function of intonation with respect to the whole conversational interaction taking into account the discourse dialogue history see also NUM
at some point in the interaction the system produces a sentence like sie fahren um drei uhr von darmstadt nach heidelberg you travel at three o clock from darmstadt to heidelbei g
to illustrate the algorithm let s consider the following sentence lafarge coppee said it would buy NUM percent in national gypsum the number two plasterboard company in the us a purchase which allows it to be present on the world s biggest plasterboard market
we lind that dedina and nusbaum s reported error rate of NUM can not be reproduced our figure is about two or three times that
accordingly we have tested the pba implementations by removing each word in turn from its relevant database and obtaining a pronunciation by analogy with the remainder
the clear expectation given the crude nature of their alignment is that they should have experienced a higher error rate not a dramatically lower one
if one candidate corresponds to a unique shortest path in terms of number of arcs through the lattice this is selected as the output
this factor excludes only the most frequent words from further consideration
this allows the system to learn successfully from very sparse data
each line shows the filename the title of the text the number of common words and the value for r
besides no classification system is NUM reliable so techniques that are based on them will inherit this uncertainty
conversely ff the test text is very different from the training text then the perplexity will be high
the issue here is essentially one of quality since it is shown that not all domain specific corpora are equal
any difference greater than about NUM NUM is therefore significant and there are many pairs for which this is true
the first concerns the problem of acquiring a suitable sample of the domain specific language data from which to train the models
secondly the method by which similarity is measured should ideally be independent to the method by which success is evaluated
evidently this data is not actually spoken email but its domain and genre are nevertheless closely related to email
the results of the language modelling exercise provide clear evidence that it is possible to build effective lms from small corpora
figure NUM the drug experiment the change in the disambiguation performance with iteration number is plotted separately for each sense
in this section we adopt some of their most relevant results
we now briefly address l he syntactic aspects of french sentential negation
since ncg is located between t and agr this projection is agrp
the lprench clause representations we use are rather classical
here of course this reflmal could probably be associated with
NUM un gars voyait marie a boy was seeing mary
this tense typically introduces an event rather than a state
it is encouraging that lexical accommodation happens spontaneously
in order to headphones in the telephone condition
we can take advantage of even the moderately high level of accommodation found in the machinemediated setting by building into a language processing system a preference for the lexical items used by the machine
when we examined the percentage of words in common used first by each role agent or client the following patterns emerged figure NUM data were subjected to three way analyses of variance
that is once one conversant accommodates to the other by adopting a lexical item does that conversant continue to use that lexical item in a significant way in the remainder of the conversation
the machine interpreted setting is an even more stressful communication environment than the human interpreted setting concern for communicational efficiency resulted in a higher level of accommodation than concern for social standing did in the human human setting
more specifically the human interpreted setting involved speakers from two different language backgrounds both of whom were capable of recognizing the differences in their linguistic behaviors and of reducing those differences to facilitate communication
however given the fact that there was a lower rate of accommodation than in the human interpreted setting coupled with the strong directionality observed we conclude that this is not a case of mutual accommodation
our experiments show that each file from semcor has a different behavior c f
the results show that our algorithm performs better on the test set
finally sections NUM and NUM deal with further work and conclusions
senses tree concept end oop output disambjguatj on
figure NUM context size and different filcs
figure NUM sense level vs file level
extend and improve the semantic data
in the experiments we considered these partial outcomes as failure to disambiguate
all of them add extra machinery and some add extra expressive power to the core mechanism
unlike lexical rules ou approach does rcb lot face any blocking problem
NUM john put the shelf with his shoes
to avoid this the following solution may be adopted
NUM the peasant loaded the horses on the boat
only one trivalent underspecified version of to load is necessary
such type resolving clauses are provided for each alternation pattern
NUM john put his shoes on the shelf
NUM mary stuffed the feathers into the pillow
considerable ambiguity is introduced with unpleasant results for parsing time
a constituent boundary is expressed by either a functional word or a part of speech bigram marker e.g.
also important is the introduction of a repair mechanism to correct the i est first results
verb phrase vp vp np verb
we obtain indices to patterns from each word of the sentence
NUM you can walk there in about three minutes
linguistic level sublevels of variables simple sentence vp np
table NUM distance calculation in x at f
they can be easily processed by slightly extending the algorithm
table NUM shows the translation time of the above sentences
NUM will my laundry be ready by tomorrow
the following steps are based on our experience and we believe will extend to a wide range of language types
finally we need an alternative form for pronouns thai refer to dynamic individuals hen NUM where NUM dr ant he the pronoun hen recovers xn from the current context
the word suicide however which should be tagged as a verb was improperly tagged as a noun
a primary insight of dynamic semantics is that sentences have a systematic relation to context in two ways not only are they evaluated with respect to the current context but they also systematically change that context
the dynamic theory explains all four of these eases in the same way the embedded proform in the antecedent an be sloppy because the controller for the embedded proform can undergo a center shift
center to the system we will allow dynamic properties to be added to contexts as antecedents for vp ellipsis we will allow dynamic individuals to be added to contexts to accoullt for paycheck prollolllls
w will apply a v ry siinplili d version ot entering ttmory consisting o l h following conslra ints
of ua no l ayeheck u NUM save u4 ua the dynamic individual xa adds the paycheck of u0 the discourse center to the context
as an example let us consider the 5th fine of table NUM where the number NUM is marked with a square
recall was computed as the percent of all postagged strings in the edr corpus that were successfully identified by our algorithm as words and as belonging to the correct pos
sign noun pp noun pp adj
noun pp pp noun pp verb in i
using genotypes at the unigram level tends to result in overgeneralization due to the fact that the genotype sets are too coarse
NUM NUM conditions of the exi eriments
table NUM environment of the noun
figure NUM an example of the different sorts of er
the one to one assumption implies independence between different link types so that
by recognizing an utterance to be ironic the hearer becomes aware of an illocutionary act of irony that of conveying the fact that the utterance situation is surrounded by ironic environment i.e. all the three components for ironic environment hoht in a current situation
this step significantly reduces the computational burden of the algorithm
certainly there are cases where this assumption is false
would be the winners in any competitions involving u or v
equipment grant from sun microsystems and by arpa contract n NUM 94c NUM
table NUM erroneous link tokens generated by two translation models
in practice these imum in the region of interest
although this set of instances does not ret resent all tile information that could in principle be derived from the smalltalk specitications of the editor application it nevertheless simplifies greatly the technical author s task of knowledge specification by providing the huilding blocks from which higher level procedures can be defined
because user documentation frequently inchldes information other than the raw actions to be performed our representation allows authors to include information not typically foulld in traditional plan rel resentations such as 1seroriented motiw tional goals helpflfl si le efl e ts and general colllliletlts
in our examt le then we build a visualworks mock up of our word processing application and i rai tek derives task model instmmes for all the windows and widgets in he application e.g. the save as dialog box and all its widget s directly fl om tile smalltalk source code
in the figure this cnl text is reader save current document which could be expressed in english in a mnnber of ways including save the current document and to save tile document
we found that the mdl based method performs better than the mle based method
but we will leave NUM implicit
these results can be interpreted as follows
object the a m biguity of the object pointing nmst be solved by comparing the two mode interpreta tions
a nd provides mm cg a gramma ticm framework for multi roods NUM input interpreta tion
o ly frequently used ex ressi us should be selected ca refi lly
the tra ns la tor of mm i cg to prolog predica tes
there re a mbiguities which a re solved only by integrating pa rtim interpreta tions of rehtted modes
method has been inductively defiiie t through several experhnent a NUM i milt i inodaj int erfa ce
hclp language is similar to constraint h gic progralnming bmguage except that we nil represent a constraint hierm dly
among variants of circumscription prioritizcd circu mscriptio n is suitable to ret rcsent various strength of preference rules
now we discuss an imt lementation of prioritized iv ulns ritfl ion by iici p
in or tot to dcmons ratc defea sibilil y of t rcfer o NUM
in this re uting a legist he following preference rule of mmther inertia of possession is used
searching the interpretation model proceeds in two phases
in our experiment on ill f mned sentences ill technical do ulnents in more than h flf of the incoml letely NUM trsed sentences the lmrt im
without constructing a i recige filodel of the eohtext through deep sema nficamtlys is our frmne work refers to a set ff parsed trees
mioreover the word sense of h ave in the subordinar claus can not NUM e sch t d without infl rma tion
figure NUM translation with context ii
the set of rules forms the program that directs the interaction of the different components given the users input
described event e c at domain event e is described as an event having content c an t attitude at toward e is also described
they reflect the results of a discourse structure analysis which show that speakers tend to distribute the constituents of a domain action into different ius by using ei aboration
