  We investigate the incorporation of character-based word representations into
a standard CNN-based relation extraction model. We experiment with two common
neural architectures, CNN and LSTM, to learn word vector representations from
character embeddings. Through a task on the BioCreative-V CDR corpus,
extracting relationships between chemicals and diseases, we show that models
exploiting the character-based word representations improve on models that do
not use this information, obtaining state-of-the-art result relative to
previous neural approaches.
