Attention-Wrapped Hierarchical BLSTMs for DDI Extraction
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...
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Veröffentlicht in: | arXiv.org 2019-07 |
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Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 2331-8422 |