Friday, September 28, 2007
11:00 AM
Computer Science Bldg. Room 601
Ph.D. Thesis Proposal
Benjamin Van Durme
University of Rochester
Conditional Knowledge Acquisition
Research in Information Extraction has been focused on the extraction of
facts concerning individuals as compared to general knowledge pertaining to classes of
entities and events. Today's extraction systems are built assuming large, heterogeneous
collections of text, and are based on simple techniques enabling high volume throughput.
We propose to extend the state of the art in Information Extraction by placing
greater focus on language understanding, the knowledge representation being targeted,
and the underlying data upon which our system will run. We specifically target the
extraction of conditional relationships tending to hold between episodes.