Document-level Relation Extraction via Separate Relation Representation and Logical Reasoning
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Published:2023-08-21
Issue:1
Volume:42
Page:1-24
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ISSN:1046-8188
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Container-title:ACM Transactions on Information Systems
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language:en
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Short-container-title:ACM Trans. Inf. Syst.
Author:
Huang Heyan1ORCID,
Yuan Changsen1ORCID,
Liu Qian2ORCID,
Cao Yixin3ORCID
Affiliation:
1. Beijing Institute of Technology, China
2. Nanyang Technological University, Singapore
3. Singapore Management University, Singapore
Abstract
Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, we propose a novel model,
SRLR
, using
S
eparate Relation
R
epresentation and
L
ogical
R
easoning considering the indirect relation representation and complex reasoning of evidence sentence problems. Specifically, we first expand the judgment of relational facts from the entity-level to the mention-level, highlighting fine-grained information to capture the relation representation for the entity pair. Second, we propose a logical reasoning module to identify evidence sentences and conduct relational reasoning. Extensive experiments on two publicly available benchmark datasets demonstrate the effectiveness of our proposed SRLR as compared to 19 baseline models. Further ablation study also verifies the effects of the key components.
Funder
National Natural Science Foundation of China
Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant
Beijing Institute of Technology Southeast Academy of Information Technology
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
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