Computer Science ›› 2022, Vol. 49 ›› Issue (4): 282-287.doi: 10.11896/jsjkx.210200027

• Artificial Intelligence • Previous Articles     Next Articles

Chinese Short Text Classification Algorithm Based on Hybrid Features of Characters and Words

LIU Shuo, WANG Geng-run, PENG Jian-hua, LI Ke   

  1. People's Liberation Army Strategic Support Force Information Engineering University, Zhengzhou 450000, China
  • Received:2021-02-02 Revised:2021-05-31 Published:2022-04-01
  • About author:LIU Shuo,born in 1996,postgraduate.His main research interests include data analysis,natural language processing and short text classification.WANG Geng-run,born in 1987,Ph.D,assistant researcher.His main research interests include telecommunication network security and data processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61803384).

Abstract: The rapid development of information technology has lead to massive data of Chinese short texts on the Internet.As such, using classification technology to dig out valuable information from it is a current research hotspot.Compared with Chinese long texts, short texts have the characteristics of fewer words, more ambiguities and irregular information, making text feature extraction and expression a challenge.For this reason, a Chinese short text classification algorithm based on the deep neural network model of hybrid features of characters and words is proposed.First, the character vector and word vector of Chinese short text are calculated respectively.Then, their features are extracted and fused.Last, the classification task is accomplished through the fully connected layer and the softmax layer.The test results on the public THUCNews news data set show that the algorithm is better than the mainstream TextCNN, BiGRU, Bert and ERNIE_BiGRU comparison models in terms of accuracy, recall and F1 value.It has a good effect on short text classification.

Key words: Character vector, Chinese short text classification, Convolutional Neural Network, Pre-training model, Word vector

CLC Number: 

  • TP391.1
[1] SHI H M.Research on Social Network Information Filtering Method Based on Long Short-term Memory Network [D].Nanjing University of Posts and Telecommunications,2019.
[2] ZHAO J Q.Research on Internet Public Opinion MonitoringMethod Based on Automatic Classification[J].Software Guide,2016,15(3):133-135.
[3] WU S,GAO M,XIAO Q,et al.A topic-enhanced recurrent autoencoder model for sentiment analysis of short texts[J].International Journal of Internet Manufacturing and Services,2020,7(4):393-399.
[4] CHEN H.Personalized recommendation system of e-commercebased on big data analysis[J].Journal of Interdisciplinary Ma-thematics,2018,21(5):1243-1247.
[5] TAN C.Short Text Classification Based on LDA and SVM[J].International Journal of Applied Mathematics & Stats,2013,51(22):205-214.
[6] YIN C,SHI L,WANG J.Short Text Classification Technology Based on KNN+Hierarchy SVM[C]//International Conference on Multimedia and Ubiquitous Engineering International Conference on Future Information Technology.2017:633-639.
[7] MINAEE S,KALCHBRENNER N,CAMBRIA E,et al.Deeplearning based text classification:A comprehensive review[J].arXiv:2004.03705,2020.
[8] LI C B,DUAN Q J,JI C H,et al.Method of Short Text Classification Based on CHI and TF-IWF Feature Selection[J].Journal of Chongqing University of Technology(Natural Science),2021,35(5):135-140.
[9] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estimation of Word Representations in Vector Space[C]//Proceedings of the International Conference on Learning Representations.ACM,2013:1-8.
[10] MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrasesand their compositionality[C]//Advances in Neural Information Processing Systems.2013:3111-3119.
[11] PETERS M,NEUMANN M,IYYER M,et al.Deep contextua-lized word representations[C]//Proceedings of the 2018 Confe-rence of the North American Chapter of the Association for Computational.2018:2227-2237.
[12] DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[C]//Proceeding of the 2019 Conference of the North American Chapter of the Association for Computational Linguistices (NAACL).2019:4171-4186.
[13] LAN Z,CHEN M,GOODMAN S,et al.ALBERT:A LiteBERT for Self-supervised Learning of Language Representations[EB/OL].(2019-09-26)[2020-01-06].
[14] ZHANG Z Y,HAN X,LIU Z Y,et al.ERNIE:enhanced language representation with informative entities[C]//Proceedings of the 57th Annual Meeting of the Association for Computatio-nal Linguistics.Florence,2019:1441-1451.
[15] SUN Y,WANG S,LI Y,et al.ERNIE 2.0:A Continual Pre-Training Framework for Language Understanding[J].Procee-dings of the AAAI Conference on Artificial Intelligence,2020,34(5):8968-8975.
[16] KIM Y.Convolutional Neural Networks for Sentence Classification[C]//Association for Computational Linguistics.Procee-dings of the 2014 Conference on Empirical Methods in Natural Language Processing.Doha,Qatar,2014:1746-1751.
[17] JOHNSON R,ZHANG T.Deep Pyramid Convolutional Neural Networks for Text Categorization[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.2017:562-570.
[18] LI Z J,GENG C Y,SONG P.Research on Short Text Classification Based on LSTM-TextCNN Joint Model[J].Journal of Xi’an Technological University,2020,40(3):299-304.
[19] DUAN D D,TANG J S,WEN Y,et al.Research on ChineseShort Text Classification Algorithm Based on BERT[J].Computer Engineering,2021,47(1):79-86.
[20] ZHENG C,HONG T T,XUE M Y.BLSTM_MLPCNN Model For Short Text Classification[J].Computer Science,2019,46(6):206-211.
[21] HOU X L,LI X,CHEN Y P.Short Text Classification Model Based on Multi-Neural Network Hybrid[J].Computer System Applications,2020,29(10):9-19.
[22] SUN M S,LI J Y,GUO Z P,et al.THUCTC:An efficient toolkit for Chinese text classification [EB/OL].
[23] HU D F,ZHANG C X,WANG S T,et al.Intelligent Prediction Model of Tool Wear Based on Deep Signal Processing and Stacked-ResGRU[J].Computer Science,2021,48(6):175-183.
[24] WANG W,SUN Y X,QI Q J,et al.Text sentiment classification model based on BiGRU-attention neural network[J].Application Research of Computers,2019,36(12):3558-3564.
[25] LEI J S,QIAN Y.Chinese text classification method based on ERNIE-BiGRU model[J].Journal of Shanghai Electric Power University,2020,36(4):329-335,350.
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