Year: 2019
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Mostafapour, Vahab, Dikenelli, Oğuz
Institution: Ege University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W2966564913