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

Dat Quoc Nguyen, Karin Verspoor
2018 arXiv   pre-print
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
more » ... -of-the-art result relative to previous neural approaches.
arXiv:1805.10586v1 fatcat:m5axhs7hrfhz5kmvhxz2vvljza