@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 = "{B}io{NLP} 2017",
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.",
}
Markdown (Informal)
[Extracting Drug-Drug Interactions with Attention CNNs](https://aclanthology.org/W17-2302) (Asada et al., BioNLP 2017)
ACL