Weakly-supervised relation extraction by pattern-enhanced embedding learning

Meng Qu, Xiang Ren, Yu Zhang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 corpus-level co-occurrence statistics of entities to predict their relations, and require a large number of labeled instances to learn effective relation classifiers. 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, unified 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-confident 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.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
PublisherAssociation for Computing Machinery, Inc
Pages1257-1266
Number of pages10
ISBN (Electronic)9781450356398
DOIs
StatePublished - Apr 10 2018
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: Apr 23 2018Apr 27 2018

Publication series

NameThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
CountryFrance
CityLyon
Period4/23/184/27/18

Keywords

  • Co-training
  • Relation extraction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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