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- W4289335874 abstract "Aiming at the problems of short text length, less information contained in text and poor classification performance caused by easy interference of deep learning model, combined with the advantages of CNN and BiLSTM, a text classification method based on fusion semantics, attention mechanism and BiLSTM-CNN model is proposed. Firstly, the Skip-gram model in word2vec is used to map the words in the data and convert them into vectors with fixed dimensions as the input of the model. The local features of the text vector are extracted by CNN and the global features related to the text context are extracted by BiLSTM. The features extracted by the two complementary models are fused, It solves the problem that the single convolution neural network model ignores the semantic and grammatical information of words in the context, and effectively avoids the gradient disappearance or gradient dispersion of the traditional cyclic neural network. This paper makes an experimental analysis on R8 and Ohsumed datasets. The results show that the proposed feature fusion model effectively improves the accuracy of text classification." @default.
- W4289335874 created "2022-08-02" @default.
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- W4289335874 date "2022-08-01" @default.
- W4289335874 modified "2023-10-18" @default.
- W4289335874 title "Research on short text classification method based on semantic fusion and BiLSTM-CNN" @default.
- W4289335874 doi "https://doi.org/10.1117/12.2640209" @default.
- W4289335874 hasPublicationYear "2022" @default.
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