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Extracting Drug-Drug Interactions with Attention CNNs - ACL Anthology
Correct Metadata for
Abstract
We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12%, which is competitive with the state-of-the-art models.- Anthology ID:
- W17-2302
- Volume:
- Proceedings of the 16th BioNLP Workshop
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9–18
- Language:
- URL:
- https://aclanthology.org/W17-2302/
- DOI:
- 10.18653/v1/W17-2302
- Bibkey:
- Cite (ACL):
- Masaki Asada, Makoto Miwa, and Yutaka Sasaki. 2017. Extracting Drug-Drug Interactions with Attention CNNs. In Proceedings of the 16th BioNLP Workshop, pages 9–18, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Extracting Drug-Drug Interactions with Attention CNNs (Asada et al., BioNLP 2017)
- Copy Citation:
- PDF:
- https://aclanthology.org/W17-2302.pdf
Export citation
@inproceedings{asada-etal-2017-extracting,
title = "Extracting Drug-Drug Interactions with Attention {CNN}s",
author = "Asada, Masaki and
Miwa, Makoto and
Sasaki, Yutaka",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 16th {B}io{NLP} Workshop",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2302/",
doi = "10.18653/v1/W17-2302",
pages = "9--18",
abstract = "We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12{\%}, which is competitive with the state-of-the-art models."
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<abstract>We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12%, which is competitive with the state-of-the-art models.</abstract>
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%0 Conference Proceedings %T Extracting Drug-Drug Interactions with Attention CNNs %A Asada, Masaki %A Miwa, Makoto %A Sasaki, Yutaka %Y Cohen, Kevin Bretonnel %Y Demner-Fushman, Dina %Y Ananiadou, Sophia %Y Tsujii, Junichi %S Proceedings of the 16th BioNLP Workshop %D 2017 %8 August %I Association for Computational Linguistics %C Vancouver, Canada, %F asada-etal-2017-extracting %X We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12%, which is competitive with the state-of-the-art models. %R 10.18653/v1/W17-2302 %U https://aclanthology.org/W17-2302/ %U https://doi.org/10.18653/v1/W17-2302 %P 9-18
Markdown (Informal)
[Extracting Drug-Drug Interactions with Attention CNNs](https://aclanthology.org/W17-2302/) (Asada et al., BioNLP 2017)
- Extracting Drug-Drug Interactions with Attention CNNs (Asada et al., BioNLP 2017)
ACL
- Masaki Asada, Makoto Miwa, and Yutaka Sasaki. 2017. Extracting Drug-Drug Interactions with Attention CNNs. In Proceedings of the 16th BioNLP Workshop, pages 9–18, Vancouver, Canada,. Association for Computational Linguistics.