research-article

Chinese Text Feature Extraction and Classification Based on Deep Learning

Authors:

Ruishuang Wang,

Tong ChenAuthors Info & Claims

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

Article No.: 89, Pages 1 - 5

https://doi.org/10.1145/3331453.3361636

Published: 22 October 2019 Publication History

Abstract

With the rapid development of deep learning, neural networks have been widely used in natural language processing tasks and achieved good results. Since convolutional neural networks can acquire high-level features that can better represent textual semantic information, convolutional neural networks (CNN) and convolutional recurrent neural networks (CRNN) are used to establish feature extraction models to extract text features. At the same time, tf-idf and word2vec methods are used to represent text features, and then feed them into SVM and Random forest classifier to classify Chinese academic papers dataset. Experimental results show that the classification results obtained by using the CNN and CRNN feature extraction model are better than using the TF-IDF and Word2vec feature extraction methods. In addition, the classification results obtained by using SVM and Random forest classifier are better than that of the original neural network.

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Index Terms

Chinese Text Feature Extraction and Classification Based on Deep Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Other conferences

CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering

October 2019

942 pages

ISBN:9781450362948

DOI:10.1145/3331453

Conference Chair:
Ali Emrouznejad,
Program Chair:
Zeshui Xu

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 October 2019

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CSAE 2019

CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering

October 22 - 24, 2019

Sanya, China

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Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Wang NZhang SZhang ZFu JLiu JWang R(2023)Block-Based Privacy-Preserving Healthcare Data Ranked Retrieval in Encrypted Cloud File SystemsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.321268427:2(732-743)Online publication date: Feb-2023
https://doi.org/10.1109/JBHI.2022.3212684
Yang TDang YWu J(2023)How to prioritize perceived quality attributes from consumers' perspective? Analysis through social media dataElectronic Commerce Research10.1007/s10660-022-09652-7Online publication date: 10-Jan-2023
https://doi.org/10.1007/s10660-022-09652-7
Wang NJiao JZhang SLiu JZheng KFu JQiao J(2023)Privacy-Preserving Searchable Encryption Scheme Based on Deep Structured Semantic Model over Cloud ApplicationMachine Learning for Cyber Security10.1007/978-3-031-20099-1_49(584-608)Online publication date: 13-Jan-2023
https://doi.org/10.1007/978-3-031-20099-1_49
Lu YLuo JXiao YZhu H(2021)Text representation model of scientific papers based on fusing multi-viewpoint information and its quality assessmentScientometrics10.1007/s11192-021-04028-4126:8(6937-6963)Online publication date: 23-Jun-2021
https://dl.acm.org/doi/10.1007/s11192-021-04028-4

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