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Social Network Analysis and Mining
Published in:

01-12-2023 | Original Article

Relation extraction: advancements through deep learning and entity-related features

Authors: Youwen Zhao, Xiangbo Yuan, Ye Yuan, Shaoxiong Deng, Jun Quan

Published in: Social Network Analysis and Mining | Issue 1/2023

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Abstract

The article delves into the critical task of relation extraction in natural language processing, focusing on the challenges of inaccurate entity pair recognition and feature sparsity. It introduces a deep learning framework that integrates entity-related features to address these issues. The proposed method involves extracting unit features from target entity pairs and combining them into fusion features to enrich semantic information. The framework uses both convolutional and graph convolutional neural networks to capture local and global dependencies in sentences. Experimental results on various datasets showcase the effectiveness of this approach, achieving high F1-scores and outperforming existing methods. The article also discusses the importance of considering the structure of target entity pairs and incorporating multiple semantic features to enhance relation extraction performance.
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Footnotes
1
In this study, the SanWen dataset does not include negative instances.
 
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Metadata
Title
Relation extraction: advancements through deep learning and entity-related features
Authors
Youwen Zhao
Xiangbo Yuan
Ye Yuan
Shaoxiong Deng
Jun Quan
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-023-01095-8

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