Paper: Towards Accurate Distant Supervision for Relational Facts Extraction

ACL ID P13-2141
Title Towards Accurate Distant Supervision for Relational Facts Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

Distant supervision (DS) is an appealing learning method which learns from exist- ing relational facts to extract more from a text corpus. However, the accuracy is still not satisfying. In this paper, we point out and analyze some critical factors in DS which have great impact on accuracy, including valid entity type detection, negative training examples construction and ensembles. We propose an approach to handle these factors. By experimenting on Wikipedia articles to extract the facts in Freebase (the top 92 relations), we show the impact of these three factors on the accuracy of DS and the remarkable im- provement led by the proposed approach.