A Character-Level Method for Text Classification
Hua Qu, Shi Qundong, Dingchao Jiang, Guo Lei, Yanpeng Zhang, Liu Pengkang · 2018
We propose a language model of mix CNN (Convolution Neural Network) with bi-RNN (Bi-directional Recurrent Neural Network) to classify the text at the character-level. Unlike word-level model is that avoiding the problem of unregistered words and improves the robustness of the text representation in character-level model. The language model mainly uses the data augment by different convolution filters of CNN and then the bi-RNN obtain the contextual information in both directions to classify the text. The results show that this model have a better performance than the common CNN and LSTM(long short-term memory) classification methods.