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2019 ACL ACL 2019

Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings

Abstract

AbstractThis paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.

🌉 Interdisciplinary Bridge — Deep Learning and Healthcare & Medicine and Natural Language Processing
🐝 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

Segun Taofeek Aroyehun , Alexander Gelbukh

Topics

Deep Learning > Architectures > Neural Networks Natural Language Processing > Applications > Text Classification Natural Language Processing > Applications > Named Entity Recognition Deep Learning > Learning Types > Representation Learning Healthcare & Medicine > Clinical > Medical NLP

Keywords

text classification long short-term memory word embedding social media adverse drug reaction social media mining
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