A hybrid network combining per-word convolution, two LSTM paths, and attention matches or beats recurrent-convolutional baselines on seven text tasks with fewer parameters.
In Proceedings of the 2013 conference on empirical methods in natural language processing , pages 1631–1642
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Sequential Learning of Convolutional Features for Effective Text Classification
A hybrid network combining per-word convolution, two LSTM paths, and attention matches or beats recurrent-convolutional baselines on seven text tasks with fewer parameters.