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

Dat Quoc Nguyen, Karin Verspoor


Abstract
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.
Anthology ID:
W18-2314
Volume:
Proceedings of the BioNLP 2018 workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | BioNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
129–136
Language:
URL:
https://aclanthology.org/W18-2314
DOI:
10.18653/v1/W18-2314
Bibkey:
Cite (ACL):
Dat Quoc Nguyen and Karin Verspoor. 2018. Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings. In Proceedings of the BioNLP 2018 workshop, pages 129–136, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings (Nguyen & Verspoor, 2018)
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PDF:
https://aclanthology.org/W18-2314.pdf