Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (3): 49-56.doi: 10.16088/j.issn.1001-6600.2021071001

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Semantic Similarity Computing Model for Short Text Based on Deep Learning

ZHOU Shengkai1,2, FU Lizhen1,2*, SONG Wen’ai1,2   

  1. 1. School of Software, North University of China, Taiyuan Shanxi 030051, China;
    2. Shanxi Military and Civilian Intergration Software Engineering Technology Research Center, Taiyuan Shanxi 030051, China
  • Received:2021-07-10 Revised:2021-11-14 Online:2022-05-25 Published:2022-05-27

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Abstract: Short text semantic similarity measurement based on deep learning is the cornerstone of modern natural language processing, and its importance is self-evident. Text encoding model is proposed in this paper based on convolutional neural network and bidirectional gated circulation unit, by convolution important semantic extraction and through bidirectional gated circulation unit to ensure semantic sequence cycles. And the consistency of text encoding is ensured by Siamese neural network structure. In this paper, traditional convolution neural networl is compared with both short-term and long-term memory network and BERT model. Experimental results are done on Quora data set, Sick data set and MSRP data set. The verification results show that the accuracy and recall rate of the proposed model are excellent, and the comprehensive performance index F1 value is the best compared with the traditional model.

Key words: natural language processing, semantic similarity, convolutional neural network, long short-term memory, gated recurrent unit

CLC Number: 

  • TP391.1
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