Publication Type

Journal Article

Version

publishedVersion

Publication Date

11-2024

Abstract

Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works from three perspectives, i.e., text representation, context encoding, and triplet prediction. Third, we discuss several important challenges faced by RE and summarize potential techniques to tackle these challenges. Finally, we outline some promising future directions and prospects in this field. This survey is expected to facilitate researchers’ collaborative efforts to address the challenges of real-world RE systems.

Keywords

Computing methodologies, Natural language processing, Neural networks

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

ACM Computing Surveys

Volume

56

Issue

11

First Page

1

Last Page

39

ISSN

0360-0300

Identifier

10.1145/3674501

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1145/3674501

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