Short text similarity calculation method based on word vector attention mechanism and CNN-BiPLSTM

KeXiang Huang; LinTao Yu; Yifeng Zhu; Zhilong Zhang

doi:10.1117/12.2668314

20 April 2023 Short text similarity calculation method based on word vector attention mechanism and CNN-BiPLSTM

KeXiang Huang, LinTao Yu, Yifeng Zhu, Zhilong Zhang

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126022B (2023) https://doi.org/10.1117/12.2668314
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Similarity calculation of short text is an important part of text classification. In view of the problem that statistical learning methods can only extract shallow features of short texts, but cannot express their deep semantics through the extracted features, a new method based on two-way mirror hole connection is proposed. A short text semantic similarity calculation model combining long short-term memory network (BiP-LSTM) and convolutional neural network (CNN). The word vector is pre-trained with the word vector attention mechanism, and the distance factor is introduced to solve the influence of the distance between words on the word sense association. When combining sentence-level features, an attention mechanism is added to make it contain more representative features. The experimental results show that, based on the Rotten_imdb and Quora Question Pairs datasets, the proposed short text similarity calculation method has an accuracy of 84.37% and an F1 value of 83.67%, which is better than other benchmark methods.

Citation Download Citation

KeXiang Huang, LinTao Yu, Yifeng Zhu, and Zhilong Zhang "Short text similarity calculation method based on word vector attention mechanism and CNN-BiPLSTM", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126022B (20 April 2023); https://doi.org/10.1117/12.2668314

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KEYWORDS

Semantics

Feature extraction

Education and training

Neural networks

Data modeling

Performance modeling

Correlation coefficients

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