A Relation-Specific Attention Network for Joint Entity and Relation Extraction

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Yuan, Yue
Zhou, Xiaofei
Pan, Shirui
Zhu, Qiannan
Song, Zeliang
Guo, Li
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Bessiere, C
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2020
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Abstract

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recognition followed by relation detection between every possible entity pairs, which usually suffers from numerous redundant operations. In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. Our RSAN utilizes relation-aware attention mechanism to construct specific sentence representations for each relation, and then performs sequence labeling to extract its corresponding head and tail entities. Experiments on two public datasets show that our model can effectively extract overlapping triplets and achieve state-of-the-art performance.

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Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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© 2020 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the Conference's website for access to the definitive, published version.
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Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
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Yuan, Y; Zhou, X; Pan, S; Zhu, Q; Song, Z; Guo, L, A Relation-Specific Attention Network for Joint Entity and Relation Extraction, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 4054-4060