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Molding CNNs for text: non-linear, non-consecutive convolutions
The success of deep learning often derives from well-chosen operational
building blocks. In this work, we revise the temporal convolution operation in
CNNs to better adapt it to text processing. Instead of concatenating word
representations, we appeal to tensor algebra and use low-rank n-gram tensors to
directly exploit interactions between words already at the convolution stage.
Moreover, we extend the n-gram convolution to non-consecutive words to
recognize patterns with intervening words. Through a combination of low-rank
tensors, and pattern weighting, we can efficiently evaluate the resulting
convolution operation via dynamic programming. We test the resulting
architecture on standard sentiment classification and news categorization
tasks. Our model achieves state-of-the-art performance both in terms of
accuracy and training speed. For instance, we obtain 51.2% accuracy on the
fine-grained sentiment classification task
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