Improving Distantly Supervised Relation Extraction by Knowledge Base-Driven Zero Subject Resolution

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This paper introduces a technique for automatically generating potential training data from sentences in which entity pairs are not apparently presented in a relation extraction. Most previous works on relation extraction by distant supervision ignored cases in which a relationship may be expressed via null-subjects or anaphora. However, natural language text basically has a network structure that is composed of several sentences. If they are closely related, this is not expressed explicitly in the text, which can make relation extraction difficult. This paper describes a new model that augments a paragraph with a "salient entity" that is determined without parsing. The entity can create additional tuple extraction environments as potential subjects in paragraphs. Including the salient entity as part of the sentential input may allow the proposed method to identify relationships that conventional methods cannot identify. This method also has promising potential applicability to languages for which advanced natural language processing tools are lacking.
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Issue Date
2018-10
Language
English
Article Type
Article
Citation

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E101D, no.10, pp.2551 - 2558

ISSN
1745-1361
DOI
10.1587/transinf.2017EDL8270
URI
http://hdl.handle.net/10203/254181
Appears in Collection
CS-Journal Papers(저널논문)
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