IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v10y2018i11p113-d184238.html
   My bibliography  Save this article

Chinese Text Classification Model Based on Deep Learning

Author

Listed:
  • Yue Li

    (School of Computer Science and Technology, Donghua University, Shanghai 201620, China)

  • Xutao Wang

    (School of Computer Science and Technology, Donghua University, Shanghai 201620, China)

  • Pengjian Xu

    (School of Computer Science and Technology, Donghua University, Shanghai 201620, China)

Abstract

Text classification is of importance in natural language processing, as the massive text information containing huge amounts of value needs to be classified into different categories for further use. In order to better classify text, our paper tries to build a deep learning model which achieves better classification results in Chinese text than those of other researchers’ models. After comparing different methods, long short-term memory (LSTM) and convolutional neural network (CNN) methods were selected as deep learning methods to classify Chinese text. LSTM is a special kind of recurrent neural network (RNN), which is capable of processing serialized information through its recurrent structure. By contrast, CNN has shown its ability to extract features from visual imagery. Therefore, two layers of LSTM and one layer of CNN were integrated to our new model: the BLSTM-C model (BLSTM stands for bi-directional long short-term memory while C stands for CNN.) LSTM was responsible for obtaining a sequence output based on past and future contexts, which was then input to the convolutional layer for extracting features. In our experiments, the proposed BLSTM-C model was evaluated in several ways. In the results, the model exhibited remarkable performance in text classification, especially in Chinese texts.

Suggested Citation

  • Yue Li & Xutao Wang & Pengjian Xu, 2018. "Chinese Text Classification Model Based on Deep Learning," Future Internet, MDPI, vol. 10(11), pages 1-12, November.
  • Handle: RePEc:gam:jftint:v:10:y:2018:i:11:p:113-:d:184238
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/10/11/113/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/10/11/113/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peng Ce & Bao Tie, 2020. "An Analysis Method for Interpretability of CNN Text Classification Model," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    2. Wenkuan Li & Peiyu Liu & Qiuyue Zhang & Wenfeng Liu, 2019. "An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism," Future Internet, MDPI, vol. 11(4), pages 1-15, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:10:y:2018:i:11:p:113-:d:184238. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.