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.
@article{arxiv.1907.13561,
title = {Attention-Wrapped Hierarchical BLSTMs for DDI Extraction},
author = {Vahab Mostafapour and Oğuz Dikenelli},
journal= {arXiv preprint arXiv:1907.13561},
year = {2019}
}