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Proceedings Paper

Chinese news text classification based on attention-based CNN-BiLSTM
Author(s): Meng Wang; Qiong Cai; Liya Wang; Jun Li; Xiaoke Wang
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Paper Abstract

With the rapid development of text categorization technology, there are still some problems, such as low classification efficiency, low accuracy and incomplete extraction of text features, in the case of large amount of data and too many categorized attributes. In this paper, a hybrid model of CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long-term and Short-term Memory Neural Network) combined with Attention (Attention Mechanism) is used to classify and process long text data. CNN extracts feature information from text, then uses BiLSTM to extract context semantics information, combines Attention to distribute weight of text information, and enters softmax classifier to classify. The experimental results show that the feature extraction of this model is more comprehensive, and the classification effect has been improved to a certain extent.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300K (14 February 2020); doi: 10.1117/12.2538132
Show Author Affiliations
Meng Wang, Wuhan Institute of Technology (China)
Qiong Cai, Wuhan Institute of Technology (China)
Liya Wang, Wuhan Institute of Technology (China)
Jun Li, Wuhan Institute of Technology (China)
Xiaoke Wang, Wuhan Institute of Technology (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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