Human-machine dialogue intention recognition after fusion of global positional information of sentence text intention classification

Ping Hu; Xianxin Ke; Yarong Li

doi:10.1117/12.2636373

6 May 2022 Human-machine dialogue intention recognition after fusion of global positional information of sentence text intention classification

Ping Hu, Xianxin Ke, Yarong Li

Author Affiliations +

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217610 (2022) https://doi.org/10.1117/12.2636373
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Text intent classification technology, most of them are based on training semantic word vectors to represent words to encode text. The word vectors considers semantics, grammar, etc. However, the global position information of the sentence is relatively lacking. Although the LSTM and other sequence models have short-term memory, the ability to extract global position information is still slightly insufficient. This article proposes to add global position information to the word vector to make the word vector feature it is stronger, and we have verified the text classification effect of FastText, TextCNN, TextRCNN, LSTM-Attention, GRU-Attention and other models in the medical consultation question and answer data. After adding global location information, the FastText classification effect has increased by 1.92%, and the classification effect of the TextCNN model has improved by 2.19%, the classification effect of the TextRCNN model has increased by 0.72%, the classification effect of the LSTM_Attention model has increased by 0.62%, and the classification ability of the GRU_Attention model has increased by 0.34%. From the experimental results of embedding location information into different models, we can note that the global location Compared with the traditional method, the position encoding embedding word vector method has higher classification accuracy and efficiency.

Citation Download Citation

Ping Hu, Xianxin Ke, and Yarong Li "Human-machine dialogue intention recognition after fusion of global positional information of sentence text intention classification", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217610 (6 May 2022); https://doi.org/10.1117/12.2636373

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KEYWORDS

Computer programming

Data modeling

Neural networks

Medicine

Systems modeling

Classification systems

Human-computer interaction

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