Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction
Venue
ACL 2013
Publication Year
2013
Authors
Wei Xu, Raphael Hoffmann, Le Zhao, Ralph Grishman
BibTeX
Abstract
(first author email should be xuwei@cs.nyu.edu) Abstract: Distant supervision has
attracted recent in- terest for training information extraction systems because it
does not require any human annotation but rather employs ex- isting knowledge bases
to heuristically la- bel a training corpus. However, previous work has failed to
address the problem of false negative training examples misla- beled due to the
incompleteness of knowl- edge bases. To tackle this problem, we propose a simple
yet novel framework that combines a passage retrieval model using coarse features
into a state-of-the-art rela- tion extractor using multi-instance learn- ing with
fine features. We adapt the in- formation retrieval technique of pseudo- relevance
feedback to expand knowledge bases, assuming entity pairs in top-ranked passages
are more likely to express a rela- tion. Our proposed technique significantly
improves the quality of distantly super- vised relation extraction, boosting recall
from 47.7% to 61.2% with a consistently high level of precision of around 93% in
the experiments.
