We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts features of multigranular phrases with variable-size convolution filters. We also show that pretraining MVCNN is critical for good performance. MVCNN achieves state-of-the-art performance on four tasks: on small-scale binary, small-scale multi-class and largescale Twitter sentiment prediction and on subjectivity classification.
Yin and Schütze REF also proposed a convolutional neural network structure (MVCNN) for sentence classification using the multichannel input method and a variable-size convolution operation.
Multichannel Variable-Size Convolution for Sentence Classification
ArXiv
ArXiv
Computer Science