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.
MCVNN combines diverse versions of pre-trained word embeddings and use variable-size convolution filters to extracts features REF .
Multichannel Variable-Size Convolution for Sentence Classification
ArXiv
ArXiv
Computer Science