Most of the current research on text classification studies feature extraction or global information extraction at the surface level, which considers all features as important features, greatly increasing the model computational power. The existing text classification model bi-directional long short-term memory (BiLSTM) can learn text contextual information, but cannot be targeted to the extraction of important features and special attention. This paper incorporates the attention mechanism into the BiLSTM model, so that the model in the learning of contextual information, while the model This paper integrates the attention mechanism into the BiLSTM model, which makes the model learn contextual information while extracting locally important feature information, reduces the number of meaningless text features, and finally speeds up the convergence of the model, and ultimately achieves a higher accuracy of the classification results.

Hao Qin. BiLSTM Text Classification Incorporating Attentional Mechanisms. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 13: 92-98. https://doi.org/10.25236/AJCIS.2023.061314.

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