Paper: Distant Supervision for Relation Extraction with an Incomplete Knowledge Base

ACL ID N13-1095
Title Distant Supervision for Relation Extraction with an Incomplete Knowledge Base
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Distant supervision, heuristically labeling a corpus using a knowledge base, has emerged as a popular choice for training relation ex- tractors. In this paper, we show that a sig- nificant number of ?negative? examples gen- erated by the labeling process are false neg- atives because the knowledge base is incom- plete. Therefore the heuristic for generating negative examples has a serious flaw. Building on a state-of-the-art distantly-supervised ex- traction algorithm, we proposed an algorithm that learns from only positive and unlabeled labels at the pair-of-entity level. Experimental results demonstrate its advantage over existing algorithms.