MrMep: Joint Extraction of Multiple Relations and Multiple Entity Pairs Based on Triplet Attention

Jiayu Chen, Caixia Yuan, Xiaojie Wang, Ziwei Bai


Abstract
This paper focuses on how to extract multiple relational facts from unstructured text. Neural encoder-decoder models have provided a viable new approach for jointly extracting relations and entity pairs. However, these models either fail to deal with entity overlapping among relational facts, or neglect to produce the whole entity pairs. In this work, we propose a novel architecture that augments the encoder and decoder in two elegant ways. First, we apply a binary CNN classifier for each relation, which identifies all possible relations maintained in the text, while retaining the target relation representation to aid entity pair recognition. Second, we perform a multi-head attention over the text and a triplet attention with the target relation interacting with every token of the text to precisely produce all possible entity pairs in a sequential manner. Experiments on three benchmark datasets show that our proposed method successfully addresses the multiple relations and multiple entity pairs even with complex overlapping and significantly outperforms the state-of-the-art methods.
Anthology ID:
K19-1055
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
593–602
Language:
URL:
https://www.aclweb.org/anthology/K19-1055
DOI:
10.18653/v1/K19-1055
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1055.pdf