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

• Other Research Article • Previous Articles Next Articles

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 Published:2019-12-20 Online: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.

References 14

1	CHEN Y S, JIANG H L, LI C Y, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6232-6251. doi: 10.1109/tgrs.2016.2584107.
2	LIANG H, SUN X, SUN Y L, et al. Text feature extraction based on deep learning:A review[J]. EURASIP Journal on Wireless Communications and Networking, 2017, 2017: 211. doi: 10.1186/s13638-017-0993-1.
3	SAXENA D, S K, K N. Survey paper on feature extraction methods in text categorization[J]. International Journal of Computer Applications, 2017, 166(11): 11-17. doi: 10.5120/ijca2017914145.
4	MENG Y, SHEN J M, ZHANG C, et al. Weakly-supervised hierarchical text classification[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33: 6826-6833. doi: 10.1609/aaai.v33i01.33016826.
5	YAO L, MAO C S, LUO Y. Graph convolutional networks for text classification[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33: 7370-7377. doi: 10.1609/aaai.v33i01.33017370.
6	KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar: Association for Computational Linguistics, 2014. doi: 10.3115/v1/d14-1181.
7	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90. doi: 10.1145/3065386.
8	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. Columbus,USA: IEEE, 2014. doi: 10.1109/cvpr.2014.81.
9	ZHAO Z Q, ZHENG P, XU S T, et al. Object detection with deep learning: A review[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11): 3212-3232. doi: 10.1109/tnnls.2018.2876865.
10	AIZAWA A. An information-theoretic perspective of tf-idf measures[J]. Information Processing & Management, 2003, 39(1): 45-65. doi: 10.1016/s0306-4573(02)00021-3.
11	HIEMSTRA D. A probabilistic justification for using tf×idf term weighting in information retrieval[J]. International Journal on Digital Libraries, 2000, 3(2): 131-139. doi: 10.1007/s007999900025.
12	THORSTEN J.A probabilistic analysis of the rocchio algorithm with TFIDF for text categorization[C]// Proceeding ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning.San Francisco, CA, USA : Morgan Kaufmann Publishers Inc, 1997:143-151.
13	MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[EB/OL]. [2019-06-10].http://arxiv.org/abs/1301.3781.
14	LE Q V, MIKOLOV T. Distributed representations of sentences and documents [EB/OL]. [2019-06-10]. http://arxiv.org/abs/1405.4053.

Metrics

Comments

Recommended 0

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits third parties to freely share (i.e., copy and redistribute the material in any medium or format) and adapt (i.e., remix, transform, or build upon the material) the articles published in this journal, provided that appropriate credit is given, a link to the license is provided, and any changes made are indicated. The material may not be used for commercial purposes. For details of the CC BY-NC 4.0 license, please visit: https://creativecommons.org/licenses/by-nc/4.0

[1]	ZHUANG Hao, LI Yang, TAO Mingkun. CNN-LSTM driver intention recognition method based on Attention mechanism [J]. Shandong Science, 2023, 36(2): 103-111.
[2]	LI Xiu-Li, DONG Ji-Wen, WU Rui-Hai. Research on KICA and Relief algorithms combined face recognition [J]. J4, 2011, 24(5): 85-88.

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

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References 14

Related Articles 2

Metrics

Comments

Recommended 0