@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://preview.aclanthology.org/iwcs-25-ingestion/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."
}