@inproceedings{nguyen-verspoor-2018-convolutional,
title = "Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings",
author = "Nguyen, Dat Quoc and
Verspoor, Karin",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-2314/",
doi = "10.18653/v1/W18-2314",
pages = "129--136",
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."
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%0 Conference Proceedings
%T Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
%A Nguyen, Dat Quoc
%A Verspoor, Karin
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the BioNLP 2018 workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F nguyen-verspoor-2018-convolutional
%X 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.
%R 10.18653/v1/W18-2314
%U https://aclanthology.org/W18-2314/
%U https://doi.org/10.18653/v1/W18-2314
%P 129-136
Markdown (Informal)
[Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings](https://aclanthology.org/W18-2314/) (Nguyen & Verspoor, BioNLP 2018)
ACL