Publications

• Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations. Wei Li. Thesis Proposal, 2006.

• A Continuous-Time Model of Topic Co-occurrence Trends. Wei Li, Xuerui Wang and Andrew McCallum. Workshop in the Thirteenth Conference on AI (AAAI), 2006.

• Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations. Wei Li and Andrew McCallum. 23rd International Conference on Machine Learning (ICML), 2006. (slides)

• Semi-Supervised Sequence Modeling with Syntactic Topic Models. Wei Li and Andrew McCallum. Twelfth Conference on AI (AAAI), 2005.

• A Note on Semi-Supervised Learning using Markov Random Fields. Wei Li and Andrew McCallum. Technical Report, 2004.

• Rapid Development of Hindi Named Entity Recognition Using Conditional Random Fields and Feature Induction. Wei Li and Andrew McCallum. ACM Transactions on Asian Language Information Processing, 2003.

• Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons. Andrew McCallum and Wei Li. Seventh Conference on Natural Language Learning (CoNLL), 2003.

• Answer Extraction using Language Models and Data-Mining. Wei Li. Combined Masters and Synthesis Project Report, 2003.

• QuASM: A System for Question Answering Using Semi-Structured Data. David Pinto, Michael Branstein, Ryan Coleman, W. Bruce Croft, Matthew King, Wei Li and Xing Wei. Joint Conference on Digital Libraries (JCDL), 2002.

• Question Classification Using Language Modeling. Wei Li. CIIR Technical Report, 2002.

Code

• NER is a Java-implemented named entity tagger based on Conditional Random Fields. It recognizes 7 types of entities: Person, Organization, Location, Time, Date, Money and Percent.

  Usage Example

  java -Xmx200m -jar ner.jar inputFile_1 inputFile_2 ... inputFile_n

  It will generate inputFile_1.ner, inputFile_2.ner, ..., inputFile_n.ner for the tagged text.

• PAM is a C-code implementation of the Pachinko Allocation model. Check out README.txt for usage instructions. This is a very preliminary version. A new version will be posted later.