  Drug-drug interaction (DDI) is a vital information when physicians and
pharmacists intend to co-administer two or more drugs. Thus, several DDI
databases are constructed to avoid mistakenly combined use. In recent years,
automatically extracting DDIs from biomedical text has drawn researchers'
attention. However, the existing work utilize either complex feature
engineering or NLP tools, both of which are insufficient for sentence
comprehension. Inspired by the deep learning approaches in natural language
processing, we propose a recur- rent neural network model with multiple
attention layers for DDI classification. We evaluate our model on 2013 SemEval
DDIExtraction dataset. The experiments show that our model classifies most of
the drug pairs into correct DDI categories, which outperforms the existing NLP
or deep learning methods.
