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2019 ACL ACL 2019

NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets

Abstract

AbstractThis paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.

🌉 Interdisciplinary Bridge — Deep Learning and Healthcare & Medicine and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — adverse effect
🐝 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

Javier Cortes-Tejada , Juan Martinez-Romo , Lourdes Araujo

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks Natural Language Processing > Applications > Text Classification Deep Learning > Learning Types > Deep Learning Deep Learning > Architectures > Recurrent Neural Networks Healthcare & Medicine > Clinical > Medical NLP

Keywords

text classification deep learning long short-term memory word embedding lstm neural network neural network adverse effect
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