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We investigate the incorporation of character-based word representations intoa standard CNN-based relation extraction model. We experiment with two commonneural architectures, CNN and LSTM, to learn word vector representations fromcharacter embeddings. Through a task on the BioCreative-V CDR corpus,extracting relationships between chemicals and diseases, we show that modelsexploiting the character-based word representations improve on models that donot use this information, obtaining state-of-the-art result relative toprevious neural approaches.

Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings