一种基于深度学习的中文文本特征提取与分类方法

doi:10.3976/j.issn.1002-4026.2019.06.015

Shandong Science ›› 2019, Vol. 32 ›› Issue (6): 106-111.doi: 10.3976/j.issn.1002-4026.2019.06.015

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A deep learning-based method for Chinese text-feature extraction and classification

CAO Lu-hui¹，DENG Yu-xiang²，CHEN Tong^3*，LI Zhao⁴

1. Shandong University， Jinan 250100， China；2. Shandong Financial Security and Evaluation Center，Jinan 250001， China；
3. Big Data Engineering Technology Research Center of E-Government， Jinan 250014， China； 4. Shandong Provincial Key
Laboratory of Computer Networks，Shandong Computer Science Center(National Super Computer in Jinan)， Qilu University of Technology(Shandong Academy of Sciences) ， Jinan 250014， China

Received:2019-08-28 Online:2019-12-20 Published:2019-12-11

Abstract

Abstract: This paper proposes a text-feature extraction method based on a convolutional recurrent neural network, and in the meanwhile, it also compares the statistical methods TF-IDF and Word2vec for text-feature representation. Text features are then fed into the SVM and Random forest classifier to classify the Chinese academic papers from CNKI. Experimental results show that the classification results obtained from the feature extraction models based on the convolutional neural network and convolutional recurrent neural network are better than those obtained from the TF-IDF and Word2vec feature extraction methods. Furthermore, the classification results obtained from the SVM and Random forest classifier are slightly better than those obtained from the native neural network

Key words: convolutional neural network, convolutional recurrent neural network, feature extraction, text classification

CLC Number:

TP391.1

CAO Lu-hui, DENG Yu-xiang, CHEN Tong, LI Zhao. A deep learning-based method for Chinese text-feature extraction and classification[J].Shandong Science, 2019, 32(6): 106-111.

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A deep learning-based method for Chinese text-feature extraction and classification

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