Recently deeplearning models have been shown to be capable of making remarkable performance in sentences and documents classification tasks. In this work, we propose a novel framework called AC-BLSTM for modeling setences and documents, which combines the asymmetric convolution neural network (ACNN) with the Bidirectional Long Short-Term Memory network (BLSTM). Experiment results demonstrate that our model achieves state-ofthe-art results on all six tasks, including sentiment analysis, question type classification, and subjectivity classification.
REF proposed to combine asymmetric convolution neural network with the bidirectional LSTM network.
AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
ArXiv
ArXiv
Computer Science