A Neural Network Based Text Classification with Attention Mechanism

Text classification is a basic task in Natural Language Processing field. As neural networks gains great breakthroughs in computer vision and speech recognition, neural networks based model, such as convolutional neural networks and recurrent neural networks, is also proved to be powerful in many Na...

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Bibliographic Details
Published in2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT) pp. 333 - 338
Main Author SiChen, Lu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
artificial intelligence
attention mechanism
Biological neural networks
convolutional neural network
Feature extraction
Logic gates
Natural language processing
Task analysis
Text categorization
text classification
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Summary:Text classification is a basic task in Natural Language Processing field. As neural networks gains great breakthroughs in computer vision and speech recognition, neural networks based model, such as convolutional neural networks and recurrent neural networks, is also proved to be powerful in many Natural Language Processing tasks compared to traditional approaches. Based on recurrent neural networks with attention mechanism, a new model incorporated enhanced text representation by means of convolutional neural networks is proposed to deal with text classification task. The experimental results show that the proposed model gains higher accuracy compared to the common attention based recurrent neural networks model.
DOI:10.1109/ICCSNT47585.2019.8962513