Bias Modeling for Distantly Supervised Relation Extraction
Joint Authors
Wang, Xiaolong
Xiang, Yang
Zhang, Yaoyun
Qin, Yang
Han, Wenying
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Xiang, Yang& Zhang, Yaoyun& Wang, Xiaolong& Qin, Yang& Han, Wenying. 2015. Bias Modeling for Distantly Supervised Relation Extraction. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1075218
Modern Language Association (MLA)
Xiang, Yang…[et al.]. Bias Modeling for Distantly Supervised Relation Extraction. Mathematical Problems in Engineering No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1075218
American Medical Association (AMA)
Xiang, Yang& Zhang, Yaoyun& Wang, Xiaolong& Qin, Yang& Han, Wenying. Bias Modeling for Distantly Supervised Relation Extraction. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1075218
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1075218