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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 9
2669-2673

Multilingual text classification method based on bi-directional long short-term memory and convolutional neural network

Meng Xianyan1
Cui Rongyi1
Zhao Yahui1
Fang Mingzhu2
1. Intelligent Information Processing Lab, Dept. of Computer Science & Technology, Yanbian University, Yanji Jilin 133002, China
2. Sci&Tech Information Service Centre of Yanbian Korean Autonomous Prefecture, Yanji Jilin 133002, China
DOI: 10.19734/j.issn.1001-3695.2019.04.0132

Abstract

In order to realize the text classification of different languages in the same category system and make full use of the value of multilingual text information, this paper proposed a multilanguage text classification model BiLSTM-CNN which combined bidirectional long short-term memory and convolutional neural networks. For each language, it extracted the text features through the two-way long-term memory neural network, and introduced the convolutional neural network to extract the text local information for feature optimization, so as to realize the distributed text representation of different language documents. Finally, it cascaded the text representation of each language into the softmax function prediction category. Experiments on parallel datasets of Chinese, British and Korean scientific and technological documents show that the proposed multilingual text classification model has a 4% improvement over the benchmark method, and can correctly classify any linguistic text with good expansibility.

Foundation Support

国家语委“十三五”科研规划项目(YB135-76)
延边大学外国语言文学世界一流学科建设科研项目(18YLPY13)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.04.0132
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 9
Section: Algorithm Research & Explore
Pages: 2669-2673
Serial Number: 1001-3695(2020)09-021-2669-05

Publish History

[2020-09-05] Printed Article

Cite This Article

孟先艳, 崔荣一, 赵亚慧, 等. 基于双向长短时记忆单元和卷积神经网络的多语种文本分类方法 [J]. 计算机应用研究, 2020, 37 (9): 2669-2673. (Meng Xianyan, Cui Rongyi, Zhao Yahui, et al. Multilingual text classification method based on bi-directional long short-term memory and convolutional neural network [J]. Application Research of Computers, 2020, 37 (9): 2669-2673. )

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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