Interactive attention network fusion Bi-LSTM and CNN for text classification

Yuxin Wu; Guofeng Deng

doi:10.1117/12.2638585

23 May 2022 Interactive attention network fusion Bi-LSTM and CNN for text classification

Yuxin Wu, Guofeng Deng

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122542F (2022) https://doi.org/10.1117/12.2638585
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

At present, many text classification model methods integrate Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM), although they have achieved good performance, they have not fully Taking advantage of the characteristics of these two models, this paper proposes a text classification model CR-IAN model based on interaction-level attention fusion of multi-channel CNN and Bi-LSTM. First: The local feature vectors of different latitudes of text are extracted through multi-channel CNN; At the same time: Pass the text matrix through the Bi-LSTM layer to obtain the result vector of the Bidirectional semantic learning of the text. Then: calculate the interactive attention according to the bidirectional output matrix and the local feature vector matrix. This can fully consider the characteristics of the text with semantics, the model can learn more text information. Finally: Pass the interactive attention through the fully connected layer and use the softmax activation function to get the classification result.

Citation Download Citation

Yuxin Wu and Guofeng Deng "Interactive attention network fusion Bi-LSTM and CNN for text classification", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122542F (23 May 2022); https://doi.org/10.1117/12.2638585

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KEYWORDS

Classification systems

Convolution

Neural networks

Performance modeling

Machine learning

Statistical modeling

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