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Title: Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
Extracting relations from text corpora is an important task with wide applications. However, it becomes particularly challenging when focusing on weakly-supervised relation extraction, that is, utilizing a few relation instances (i.e., a pair of entities and their relation) as seeds to extract from corpora more instances of the same relation. Existing distributional approaches leverage the corpuslevel co-occurrence statistics of entities to predict their relations, and require a large number of labeled instances to learn effective relation classiffers. Alternatively, pattern-based approaches perform boostrapping or apply neural networks to model the local contexts, but still rely on a large number of labeled instances to build reliable models. In this paper, we study the integration of distributional and pattern-based methods in a weakly-supervised setting such that the two kinds of methods can provide complementary supervision for each other to build an effective, uniffed model. We propose a novel co-training framework with a distributional module and a pattern module. During training, the distributional module helps the pattern module discriminate between the informative patterns and other patterns, and the pattern module generates some highly-conffdent instances to improve the distributional module. The whole framework can be effectively optimized by iterating between improving the pattern module and updating the distributional module. We conduct experiments on two tasks: knowledge base completion with text corpora and corpus-level relation extraction. Experimental results prove the effectiveness of our framework over many competitive baselines.  more » « less
Award ID(s):
1704532 1618481 1741317
PAR ID:
10062046
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 2018 World Wide Web Conference on World Wide Web, {WWW} 2018,
Volume:
2018
Issue:
1
Page Range / eLocation ID:
1257 to 1266
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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