Extracting Drug-Drug Interactions with Attention CNNs

By Masaki Asada, Makoto Miwa, Yutaka Sasaki. 2017

45 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval (2018, methodology): “While automated information extraction has been applied to simple relationships — such as Drug-Drug (Asada et al., 2017) or Protein-Protein (Peng and Lu, 2017); (Peng et al., 2015); (Li et al., 2017) interaction — with adequate precision and recall, clinically actionable biomarkers need to satisfy r”