IR - Information Retrieval Laboratory
Much of our Information Retrieval research over the past 30+ years has been directed at the fundamental issues of text representation, query formulation, and retrieval models that form the basis of all search engines. This research has been extended into a number of different architectures, such as web search, filtering information streams, and searching distributed databases. It has been extended into different languages in both multilingual and cross-lingual systems. It has been extended into different modes of interaction, such as long queries and graphics-based visualization techniques. It has been extended into different applications, such as question answering, social search, enterprise search, blog search, and tracking text reuse. Finally, it has been extended into different data types, such as images, speech, structured data, video, and music.
To give an idea of the variety of research topics pursued in the CIIR, we focus here on the people who have come out of our environment. Specifically, the following is a description of some of the graduates from the Information Retrieval lab:
IESL - Information Extraction and Synthesis Laboratory
Information -- A collection of facts, relations or events from which conclusions may be drawn. Knowledge that has been gathered or received.
Extraction -- Obtaining materials in concentrated, usable form from a dilluted, unusable source.
Synthesis -- The combining of separate elements or substances to form a coherent whole. Reasoning from the general to the particular; logical deduction.
Laboratory -- An organization performing scientific experimentation and research.
IESL aims to dramatically increase our ability to mine actionable knowledge from unstructured text. We are especially interested in information extraction from the Web, understanding the connections between people and between organizations, expert finding, social network analysis, and mining the scientific literature and community. We develop and employ various methods in statistical machine learning, natural language processing and information retrieval. We tend toward probabilistic approaches, graphical models, and Bayesian methods.
Biomedical Informatics Natural Language Processing (BioNLP) Laboratory
The BioNLP lab conducts research on information retrieval, machine learning, and natural language processing, with a focus on biomedical applications. Our goal is to extract information from the vast amount of unstructured data in the biomedical domain, such as electronic health record (EHR) notes and scientific articles. We have developed and built systems for biomedical question answering, adverse drug event detection, biomedical figure search, EHR note comprehension, and healthcare outcome predictions, among others
