Paper: Reducing Wrong Labels in Distant Supervision for Relation Extraction

ACL ID P12-1076
Title Reducing Wrong Labels in Distant Supervision for Relation Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

In relation extraction, distant supervision seeks to extract relations between entities from text by using a knowledge base, such as Freebase, as a source of supervision. When a sentence and a knowledge base refer to the same entity pair, this approach heuristically la- bels the sentence with the corresponding re- lation in the knowledge base. However, this heuristic can fail with the result that some sen- tences are labeled wrongly. This noisy labeled data causes poor extraction performance. In this paper, we propose a method to reduce the number of wrong labels. We present a novel generative model that directly models the heuristic labeling process of distant super- vision. The model predicts whether assigned labels are correct or wrong via its hidden vari- ables. Our experimental result...