Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2023

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 Separate Relation Representation and Logical Reasoning 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.

Keywords

document-level relation extraction, separate relation representation, logical reasoning, relational reasoning, computing methodologies, information extraction, atural language processing, mention-level

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Information Systems

Volume

42

Issue

1

First Page

1

Last Page

24

ISSN

1046-8188

Identifier

10.1145/3597610

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3597610

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