Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective

Qing Zhang, Houfeng Wang


Abstract
For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts. The essence of it is a challenging incomplete multi-label classification problem with sparse and noisy features. To address the challenge, this work presents a novel nonparametric Bayesian formulation for the task. Experiment results show substantially higher top precision improvements over the traditional state-of-the-art approaches.
Anthology ID:
D17-1192
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1808–1813
Language:
URL:
https://www.aclweb.org/anthology/D17-1192
DOI:
10.18653/v1/D17-1192
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1192.pdf