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        <title>Extracting Drug-Drug Interactions with Attention CNNs</title>
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    <name type="personal">
        <namePart type="given">Masaki</namePart>
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    <name type="personal">
        <namePart type="given">Makoto</namePart>
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    <name type="personal">
        <namePart type="given">Yutaka</namePart>
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            <title>BioNLP 2017</title>
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            <namePart type="given">Kevin</namePart>
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            <namePart type="family">Cohen</namePart>
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            <namePart type="given">Junichi</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/W17-2302</identifier>
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        <date>2017-08</date>
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%0 Conference Proceedings
%T Extracting Drug-Drug Interactions with Attention CNNs
%A Asada, Masaki
%A Miwa, Makoto
%A Sasaki, Yutaka
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F asada-etal-2017-extracting
%X 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.
%R 10.18653/v1/W17-2302
%U https://aclanthology.org/W17-2302
%U https://doi.org/10.18653/v1/W17-2302
%P 9-18

Markdown (Informal)

[Extracting Drug-Drug Interactions with Attention CNNs](https://aclanthology.org/W17-2302) (Asada et al., BioNLP 2017)

• Extracting Drug-Drug Interactions with Attention CNNs (Asada et al., BioNLP 2017)

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.