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,
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-2302.pdf
- Data
- DDI, SemEval-2010 Task 8