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Social Network Analysis and Mining

Erschienen in:

01.12.2023 | Original Article

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

verfasst von: Youwen Zhao, Xiangbo Yuan, Ye Yuan, Shaoxiong Deng, Jun Quan

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2023

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Abstract

Capturing semantics and structure surrounding the target entity pair is crucial for relation extraction. The task is challenging due to the limited semantic elements and structural features of the target entity pair within a sentence. To tackle this problem, this paper introduces an approach that fuses entity-related features under convolutional neural networks and graph convolution neural networks. Our approach combines the unit features of the target entity pair to generate corresponding fusion features and applies the deep learning framework to extract high-order abstract features for relation extraction. Experimental results from three public datasets (ACE05 English, ACE05 Chinese, and SanWen) indicate that the proposed approach achieves F1-scores of 77.70%, 90.12%, and 68.84%, respectively, highlighting its effectiveness and robustness. This paper provides a comprehensive description of the approach and experimental results.

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Titel: Relation extraction: advancements through deep learning and entity-related features
verfasst von: Youwen Zhao
Xiangbo Yuan
Ye Yuan
Shaoxiong Deng
Jun Quan
Publikationsdatum: 01.12.2023
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2023
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-023-01095-8

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