Attention-Wrapped Hierarchical BLSTMs for DDI Extraction
This work addresses the need for automated tools to extract drug-drug interactions from literature, which is crucial for biomedical research and healthcare, but it appears incremental as it builds on existing LSTM and attention methods.
The paper tackled the problem of extracting drug-drug interactions from biomedical text by proposing a novel deep-learning model that wraps hierarchical bidirectional LSTMs with two attention mechanisms, achieving a macro F1-score of 0.785 and precision of 0.80 on the DDIExtraction-2013 corpora.
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.