A Chinese short text entity linking approach based on data enhancement and multidimensional feature fusion

Yu Chen; Yue Zhang; Jiangcun Xie; Qiao Xiao

doi:10.1117/12.3038020

7 August 2024 A Chinese short text entity linking approach based on data enhancement and multidimensional feature fusion

Yu Chen, Yue Zhang, Jiangcun Xie, Qiao Xiao

Author Affiliations +

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132292W (2024) https://doi.org/10.1117/12.3038020
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Recently, with the rapid growth of the Internet, a considerable amount of textual data has emerged, injecting vitality into natural language processing. As a result of the diverse characteristics of natural language descriptions, humans and computers often face ambiguity problems when processing natural language text, and they are unable to combine context and prior knowledge to deeply understand the deep meaning of the text, which limits further intelligent processing of textual data. Entity linking, as a basic task, aims to link the entities mentioned in the textual data to the corresponding entities in the knowledge base. However, short text entity links face difficulties such as limited contextual information, irregular expressions, and incomplete grammatical structures. Furthermore, the flexibility and syntactic diversity of the Chinese language increase the challenge of understanding short Chinese texts on a deeper level. This paper puts forward a Chinese short text entity linking approach via data enhancement and multi-dimensional feature fusion. Experimental results demonstrate that our approach overtakes the baseline model on the Chinese short text entity linking dataset, indicating that our proposed model exhibits better competence and adaptability in handling Chinese short text entity linking tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yu Chen, Yue Zhang, Jiangcun Xie, and Qiao Xiao "A Chinese short text entity linking approach based on data enhancement and multidimensional feature fusion", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132292W (7 August 2024); https://doi.org/10.1117/12.3038020

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KEYWORDS

Data modeling

Education and training

Feature fusion

Performance modeling

Statistical modeling

Feature extraction

Semantics

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