Open Information Extraction (http://openie.cs.washington.edu) is
an attractive paradigm for extracting large amounts of relational facts from
natural language text in a domain-independent manner. In this talk I
describe our recent progress using this model, including our latest open
extractors, ReVerb and OLLIE, which substantially improve on the previous
state of the art. I will end with our ongoing work that uses open
extractions for various end tasks, including multi-document summarization
and unsupervised event extraction.
Mausam is a Research Assistant Professor at the Turing Center in the
Department of Computer Science at the University of Washington, Seattle. His
research interests span various sub-fields of artificial intelligence,
including sequential decision making under uncertainty, large scale natural
language processing, and AI applications to crowd-sourcing. Mausam obtained
a PhD from University of Washington in 2007 and a Bachelor of Technology
from IIT Delhi in 2001.