Paper: Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction

ACL ID P13-2117
Title Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

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...