Bidirectional LSTM with attention mechanism and convolutional layer for text classification

Abstract

Neural network models have been widely used in the field of natural language processing (NLP). Recurrent neural networks (RNNs), which have the ability to process sequences of arbitrary length, are common methods for sequence modeling tasks. Long short-term memory (LSTM) is one kind of RNNs and has achieved remarkable performance in text classification. However, due to the high dimensionality and sparsity of text data, and to the complex semantics of the natural language, text classification presents difficult challenges. In order to solve the above problems, a novel and unified architecture which contains a bidirectional LSTM (BiLSTM), attention mechanism and the convolutional layer is proposed in this paper. The proposed architecture is called attention-based bidirectional long short-term memory with convolution layer (AC-BiLSTM). In AC-BiLSTM, the convolutional layer extracts the higher-level phrase representations from the word embedding vectors and BiLSTM is used to access both the preceding and succeeding context representations. Attention mechanism is employed to give different focus to the information outputted from the hidden layers of BiLSTM. Finally, the softmax classifier is used to classify the processed context information. AC-BiLSTM is able to capture both the local feature of phrases as well as global sentence semantics. Experimental verifications are conducted on six sentiment classification datasets and a question classification dataset, including detailed analysis for AC-BiLSTM. The results clearly show that AC-BiLSTM outperforms other state-of-the-art text classification methods in terms of the classification accuracy.