Multichannel Variable-Size Convolution for Sentence Classification

Understand

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.