2019
EMNLP
EMNLP 2019
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature
Abstract
AbstractIn this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the state-of-the-art performance and is among the top two systems in five of all six subtasks.
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Interdisciplinary Bridge
— Deep Learning and Healthcare & Medicine and Interdisciplinary and Machine Learning and Natural Language Processing
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Keyword Pioneer
— language representation model
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Application Areas > Knowledge Distillation
Deep Learning > Architectures > Neural Networks
Natural Language Processing > Applications > Information Extraction
Healthcare & Medicine > Research > Bioinformatics
Deep Learning > Learning Types > Fine-Tuning
Interdisciplinary > Science > Bioinformatics