  Drug-Drug Interactions (DDIs) Extraction refers to the efforts to generate
hand-made or automatic tools to extract embedded information from text and
literature in the biomedical domain.
  Because of restrictions in hand-made efforts and their lower speed,
Machine-Learning, or Deep-Learning approaches have become more popular for
extracting DDIs. In this study, we propose a novel and generic Deep-Learning
model which wraps Hierarchical Bidirectional LSTMs with two Attention
Mechanisms that outperforms state-of-the-art models for DDIs Extraction, based
on the DDIExtraction-2013 corpora. This model has obtained the macro F1-score
of 0.785, and the precision of 0.80.
