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

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

1 Jul 2020archive 2025-07-28

Yue Yuan, Xiaofei Zhou, Shirui Pan, Qiannan Zhu, Zeliang Song, Li Guo

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. Our code is available at https://github.com/Anery/RSAN

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