Document-Level Relation Extraction Based on Machine Reading Comprehension and Hybrid Pointer-sequence Labeling
Authors: 
xiaoyi wang, Jie Liu, Jiong Wang, Jianyong Duan, guixia guan, qing zhang, Jianshe ZhouAuthors Info & Claims
xiaoyi wang, Jie Liu, Jiong Wang, Jianyong Duan, guixia guan, qing zhang, Jianshe ZhouAuthors Info & ClaimsArticle No.: 100, Pages 1 - 16
Abstract
Document-level relational extraction requires reading, memorization, and reasoning to discover relevant factual information in multiple sentences. It is difficult for the current hierarchical network and graph network methods to fully capture the structural information behind the document and make natural reasoning from the context. Different from the previous methods, this article reconstructs the relation extraction task into a machine reading comprehension task. Each pair of entities and relationships is characterized by a question template, and the extraction of entities and relationships is translated into identifying answers from the context. To enhance the context comprehension ability of the extraction model and achieve more precise extraction, we introduce large language models (LLMs) during question construction, enabling the generation of exemplary answers. Besides, to solve the multi-label and multi-entity problems in documents, we propose a new answer extraction model based on hybrid pointer-sequence labeling, which improves the reasoning ability of the model and realizes the extraction of zero or multiple answers in documents. Extensive experiments on three public datasets show that the proposed method is effective.
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Index Terms
- Document-Level Relation Extraction Based on Machine Reading Comprehension and Hybrid Pointer-sequence Labeling
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Association for Computing Machinery
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Publication History
Published: 26 June 2024
Online AM: 01 June 2024
Accepted: 21 May 2024
Revised: 20 March 2024
Received: 13 March 2023
Published in TALLIP Volume 23, Issue 7
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- National Key Research and Development Program of China
- National Natural Science Foundation of China
- 14th Five-Year Scientific Research Plan of the National language commission
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