Text Classification Based on CNN-BiGRU and Its Application in Telephone Comments Recognition

Abstract

In this paper, we proposed a deep fusion model for telephone comments recognition, named CNN-BiGRU. Traditionally, the most used algorithms in text classification are Convolutional Neural Network (CNN), Long and Short Term Memory (LSTM) and Bi-Gated Recurrent Neural Network (BiGRU). For CNN, it can extract the feature form the neighbors, and a softmax layer is followed for classification. The global feature is not included in the CNN model. LSTM introduces the gate, which can capture the information before the node. BiGRU is developed from LSTM, and it can find the features in the context. So compared to LSTM, BiGRU not only includes the information before, but also can capture the following features. Thus, LSTM and BiGRU can extract the global features, but cannot capture the local features. In order to deal with this weakness, we proposed a fusion model for comments classification, which combines the CNN and BiGRU in our model. Different from other methods, CNN and BiGRU are parallelly connected. CNN model can extract the local feature, and BiGRU can find the global feature. Then we concatenate the two kinds of features and feed to recognition layer for classification. Then we use our model to classify the telephone comments; compared with the traditional machine SVM and tow deep neural models — CNN and BiGRU — our model performed better.