Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers

Understand

Document classification tasks were primarily tackled at word level.

• Recent research that works with character-level inputs shows several benefits over word-level approaches such as natural incorporation of morphemes and better handling of rare words.
• We propose a neural network architecture that utilizes both convolution and recurrent layers to efficiently encode character inputs.
• We validate the proposed model on eight large scale document classification tasks and compare with character-level convolution-only models.