research-article

Chinese Text Similarity Calculation Model Based on Multi-Attention Siamese Bi-LSTM

Authors:

Bao ZhangAuthors Info & Claims

CSSE '21: Proceedings of the 4th International Conference on Computer Science and Software Engineering

Pages 93 - 98

https://doi.org/10.1145/3494885.3494902

Published: 20 December 2021 Publication History

Abstract

Measuring text similarity is a key research area in natural language processing technology. In this research, we proposed a multi-attention Siamese bi-directional long short-term memory (MAS-Bi-LSTM) to calculate the semantic similarity between two Chinese texts. The novel model used Bi-LSTM as the basic framework of the Siamese network, introduced a multi-head attention mechanism to capture the key features of the text, and used the Manhattan distance to calculate the similarity. Experiments were conducted on the large-scale Chinese question matching corpus dataset. Results showed that our model can achieve higher accuracy compared with other comparable models. The F1 value of our model reached 0.8070. The contribution of this research is to use the multi-head attention mechanism to re-weight the semantic features, and explore the influence of different pre-training corpus, distance formulas and heads of multi-attention on the model.

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Cited By

Liu J(2024)Semantic analysis and construction of English discourse based on neural networkApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-25469:1Online publication date: 3-Sep-2024
https://doi.org/10.2478/amns-2024-2546
Zhao ZWang CJi S(2024)Text Similarity Calculation Model Based on Semantic Information and Syntactic Structure Fusion Weighting2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE)10.1109/CISCE62493.2024.10653095(531-540)Online publication date: 10-May-2024
https://doi.org/10.1109/CISCE62493.2024.10653095
Ciapas BTreigys P(2024)Centre‐loss—A preferred class verification approach over sample‐to‐sample in self‐checkout products datasetsIET Computer Vision10.1049/cvi2.1230218:7(1004-1016)Online publication date: 31-Oct-2024
https://dl.acm.org/doi/10.1049/cvi2.12302

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CSSE '21: Proceedings of the 4th International Conference on Computer Science and Software Engineering

October 2021

366 pages

ISBN:9781450390675

DOI:10.1145/3494885

Copyright © 2021 ACM.

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Publication History

Published: 20 December 2021

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Project of University Excellent Talents of Education Bureau of Anhui Province, China
Major Project of Natural Science Research Foundation of Education Bureau of Anhui Province, China

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CSSE 2021

CSSE 2021: 2021 4th International Conference on Computer Science and Software Engineering

October 22 - 24, 2021

Singapore, Singapore

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Overall Acceptance Rate 33 of 74 submissions, 45%

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Cited By

Liu J(2024)Semantic analysis and construction of English discourse based on neural networkApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-25469:1Online publication date: 3-Sep-2024
https://doi.org/10.2478/amns-2024-2546
Zhao ZWang CJi S(2024)Text Similarity Calculation Model Based on Semantic Information and Syntactic Structure Fusion Weighting2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE)10.1109/CISCE62493.2024.10653095(531-540)Online publication date: 10-May-2024
https://doi.org/10.1109/CISCE62493.2024.10653095
Ciapas BTreigys P(2024)Centre‐loss—A preferred class verification approach over sample‐to‐sample in self‐checkout products datasetsIET Computer Vision10.1049/cvi2.1230218:7(1004-1016)Online publication date: 31-Oct-2024
https://dl.acm.org/doi/10.1049/cvi2.12302

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