My research focuses on the computational foundations of intelligent behavior. We develop theories and systems pertaining to intelligent behavior using a unified methodology -- at the heart of which is the idea that learning has a central role in intelligence.
My work centers around the study of machine learning and inference methods to facilitate
natural language understanding. In doing that I have pursued several interrelated lines of work that span multiple aspects of this problem - from fundamental questions in
learning and inference and how they interact, to the study of a range of natural language processing (NLP) problems, including multiple disambiguation problems, shallow parsing, semantic role labeling, co-reference, question answering and textual entailment, to large scale Natural Language Processing and Information Extraction system development - resulting in a number of software packages for NLP tools that are available from my web site and are widely used by the community.