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
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
93–95
Language:
URL:
https://aclanthology.org/W19-3213
DOI:
10.18653/v1/W19-3213
Bibkey:
Cite (ACL):
Javier Cortes-Tejada, Juan Martinez-Romo, and Lourdes Araujo. 2019. NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 93–95, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets (Cortes-Tejada et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-3213.pdf
Data
SMM4H