Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
Dat Quoc Nguyen, Karin Verspoor
Proceedings of the BioNLP 2018 workshop | Association for Computational Linguistics | Published : 2018
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.
Related Projects (1)
Awarded by Australian Research Council
This work was supported by the ARC Discovery Project DP150101550.