Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
711-715,750

Research on short text classification model combined with word vector for sparse data

Yang Yang
Liu Enbo
Gu Chunhua
Pei Songwen
School of Optical-Electrical & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200082, China
DOI: 10.19734/j.issn.1001-3695.2021.08.0359

Abstract

Due to the lack of sufficient co-occurrence information in short text, weak connections between words, and it is difficult to obtain subject words, which leads to the need to manually label a large number of training samples for short text classification, and the problems of sparse features and dimension explosion. This paper proposed a word symbiotic short text classification model based on attention mechanism and label graph(WGA-BERT). Firstly, this paper used the pretrained BERT model to calculate the context aware text representation, and used WNTM to model the potential word group distribution of each word to obtain the topic expansion feature vector. Secondly, this paper used a tag graph construction method to capture the structure and relevance of subject words. Finally, this paper used an attention mechanism to establish the relationship between subject words and between subject words and text, which solved the problems of data sparsity and subject text heterogeneity. The experimental results show that the WGA-BERT model improves the classification accuracy by an average of 3% compared with the traditional machine learning model.

Foundation Support

国家自然科学基金资助项目(61975124)
上海自然科学基金资助项目(20ZR1438500)
上海市科委科技行动计划资助项目(20DZ2308700)
上海市经信委软件和集成电路产业发展专项(RX-RJJC-02-20-4212)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0359
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 711-715,750
Serial Number: 1001-3695(2022)03-011-0711-05

Publish History

[2021-11-29] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

杨阳, 刘恩博, 顾春华, 等. 稀疏数据下结合词向量的短文本分类模型研究 [J]. 计算机应用研究, 2022, 39 (3): 711-715,750. (Yang Yang, Liu Enbo, Gu Chunhua, et al. Research on short text classification model combined with word vector for sparse data [J]. Application Research of Computers, 2022, 39 (3): 711-715,750. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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