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Mathematical Problems in Engineering
Volume 2015, Article ID 969053, 10 pages
http://dx.doi.org/10.1155/2015/969053
Research Article

Bias Modeling for Distantly Supervised Relation Extraction

Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China

Received 24 March 2015; Accepted 11 August 2015

Academic Editor: Chih-Cheng Hung

Copyright © 2015 Yang Xiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1) bias-dist to model the relative distance between points (instances) and classes (relation centers); (2) bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-dist-classify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.