2021
EACL
EACL 2021
Leveraging knowledge sources for detecting self-reports of particular health issues on social media
Abstract
AbstractThis paper investigates incorporating quality knowledge sources developed by experts for the medical domain as well as syntactic information for classification of tweets into four different health oriented categories. We claim that resources such as the MeSH hierarchy and currently available parse information are effective extensions of moderately sized training datasets for various fine-grained tweet classification tasks of self-reported health issues.
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Interdisciplinary Bridge
— Artificial Intelligence and Healthcare & Medicine and Machine Learning and Natural Language Processing
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Keyword Pioneer
— self-reported health
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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