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- W2997152654 abstract "Traditional text classification methods are based on statistics and feature selection. It does not perform well in processing large - scale corpus. In recent years, with the rapid development of deep learning and artificial neural networks, many scholars use them to solve text classification problems and achieve good results. Common text classification neural network models include textCNN, LSTM, and C-LSTM. Using a specific model can obtain more accurate features but ignore the context information. This paper proposes a C-LSTM with word embedding model to deal with this problem. Experiments show that the model proposed in this paper has great advantages in Chinese news text classification." @default.
- W2997152654 created "2020-01-10" @default.
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- W2997152654 date "2019-06-01" @default.
- W2997152654 modified "2023-10-16" @default.
- W2997152654 title "A C-LSTM with Word Embedding Model for News Text Classification" @default.
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- W2997152654 doi "https://doi.org/10.1109/icis46139.2019.8940289" @default.
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