Extracting Drug-Drug Interactions with Attention CNNs

Masaki Asada, Makoto Miwa, Yutaka Sasaki


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:
BioNLP 2017
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 BioNLP 2017, 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
Data
DDISemEval-2010 Task-8