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Data Analysis and Knowledge Discovery
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The Multi-level Feature Extraction Capsule Network Model for Text Classification Research
YU Ben-gong,ZHU Xiao-Jie,ZHANG Zi-Wei
(School of Management, Hefei University of Technology, Hefei 230009, China) (Key Laboratory of Process Optimization & Intelligent Decision-making, Ministry of Education, Hefei University of Technology, Hefei 230009, China)
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[Objective] In order to improve the feature extraction capabilities of the existing shallow text classification models, this paper adopts a structured method to extract text information hierarchically from bottom to top, so as to improve the text classification effect.

[Methods] This paper proposes a text classification model(MFE-CapsNet)based on global and high-level feature acquisition. First, the model uses bidirectional gated recurrent unit (BiGRU) Extract the context information, and introduce the attention coding hidden layer vector to improve the feature extraction ability of the sequence model. We combine the capsule network and use dynamic routing to obtain high-level aggregated local information build an MFE-CapsNet model, and conduct comparative experiments on text classification.

[Results] The experimental results show that the F1 value of the MFE-CapsNet model proposed in this paper reaches 96.21%, 94.17%, and 94.19% on the Chinese data sets in three different fields respectively, Compared with other classification methods, the increase was at least 1.28%, 1.49%, and 0.46% respectively.

[Limitations] The experiment is only verified on three corpora.

[Conclusions] The MFE-CapsNet model uses the improved capsule network algorithm structure to more comprehensively and deeply mine the semantic features of text and improve the performance of text classification.

Key words Text classification      BiGRU      Attention       Capsule network      
Published: 29 March 2021

Cite this article:

YU Ben-gong, ZHU Xiao-Jie, ZHANG Zi-Wei. The Multi-level Feature Extraction Capsule Network Model for Text Classification Research . Data Analysis and Knowledge Discovery, 0, (): 1-.

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