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,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
9–18
Language:
URL:
https://www.aclweb.org/anthology/W17-2302
DOI:
10.18653/v1/W17-2302
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2302.pdf