A 2D Entity Pair Tagging Scheme for Relation Triplet Extraction
Authors: 
Xu Liu, Ruiqi Cai, Yonggang ZhangAuthors Info & Claims
Pages 16 - 29
Abstract
It is essential for large-scale knowledge graph construction to extract relation triplets composed of entity pairs and relations from unstructured texts. Although recent researches have made considerable progress in joint extraction of entities and relations, they are still confronted with some problems. Specifically, existing methods usually decompose the task of relation triplet extraction into several subtasks or different modules, which inevitably suffers from error propagation or poor information interaction. In this paper, a novel end-to-end relation extraction model named 2DEPT is proposed, which can effectively address the problems mentioned above. The proposed model comprises a 2D entity pair tagging scheme that provides a simple but effective decoding method and a token-pair classifier based on scoring which determines whether a token pair belongs to the specific relationship. Besides, extensive experiments on two public datasets widely used by many researchers are conducted, and the experimental results perform better than the state-of-the-art baselines overall and deliver consistent performance gains on complex scenarios of various overlapping patterns and multiple triplets. The source code and dataset are available at: https://github.com/Oliverstars/Code42DEPT.
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Information
Published In
Aug 2023
455 pages
ISBN:978-3-031-40288-3
DOI:10.1007/978-3-031-40289-0
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 16 August 2023
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