Open AccessAn Efficient Character-Level and Word-Level Feature Fusion Method for Chinese Text Classification
Author(s) -
Wei Jin,
Hong Zhu,
Yang Gao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1229/1/012057
Subject(s) - artificial intelligence , convolutional neural network , computer science , character (mathematics) , feature (linguistics) , pattern recognition (psychology) , context (archaeology) , word (group theory) , natural language processing , feature extraction , task (project management) , mathematics , geography , linguistics , engineering , philosophy , geometry , archaeology , systems engineering
In order to extract semantic feature information between texts more efficiently and reduce the effect of text representation on classification results, we propose a features fusion model C_BiGRU_ATT based on deep learning. The core task of our model is to extract the context information and local information of the text using Convolutional Neural Network(CNN) and Attention-based Bidirectional Gated Recurrent Unit(BiGRU) at character-level and word-level. Our experimental results show that the classification accuracies of C_BiGRU_ATT reach 95.55% and 95.60% on two Chinese datasets THUCNews and WangYi respectively. Meanwhile, compared with the single model based on character-level and word-level for CNN, the classification accuracies of C_BiGRU_ATT is increased by 1.6%, 2.7% on the THUCNews, and is increased by 0.6%, 5.2% on the WangYi. The results show that the proposed model C_BiGRU_ATT can extract text features more effectively.