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BRsyn-Caps: Chinese Text Classification Using Capsule Network Based on Bert and Dependency Syntax

Jie LUO, Chengwan HE, Hongwei LUO

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Summary :

Text classification is a fundamental task in natural language processing, which finds extensive applications in various domains, such as spam detection and sentiment analysis. Syntactic information can be effectively utilized to improve the performance of neural network models in understanding the semantics of text. The Chinese text exhibits a high degree of syntactic complexity, with individual words often possessing multiple parts of speech. In this paper, we propose BRsyn-caps, a capsule network-based Chinese text classification model that leverages both Bert and dependency syntax. Our proposed approach integrates semantic information through Bert pre-training model for obtaining word representations, extracts contextual information through Long Short-term memory neural network (LSTM), encodes syntactic dependency trees through graph attention neural network, and utilizes capsule network to effectively integrate features for text classification. Additionally, we propose a character-level syntactic dependency tree adjacency matrix construction algorithm, which can introduce syntactic information into character-level representation. Experiments on five datasets demonstrate that BRsyn-caps can effectively integrate semantic, sequential, and syntactic information in text, proving the effectiveness of our proposed method for Chinese text classification.

Publication
IEICE TRANSACTIONS on Information Vol.E107-D No.2 pp.212-219
Publication Date
2024/02/01
Publicized
2023/11/06
Online ISSN
1745-1361
DOI
10.1587/transinf.2023EDP7119
Type of Manuscript
PAPER
Category
Natural Language Processing

Authors

Jie LUO
  Wuhan Institute of Technology
Chengwan HE
  Wuhan Institute of Technology
Hongwei LUO
  Wuhan Institute of Technology

Keyword