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

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


Abstract
This 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.
Anthology ID:
W19-3213
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
93–95
Language:
URL:
https://www.aclweb.org/anthology/W19-3213
DOI:
10.18653/v1/W19-3213
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3213.pdf