IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification

Anne-Lyse Minard, Christian Raymond, Vincent Claveau


Abstract
This paper describes the systems developed by IRISA to participate to the four tasks of the SMM4H 2018 challenge. For these tweet classification tasks, we adopt a common approach based on recurrent neural networks (BiLSTM). Our main contributions are the use of certain features, the use of Bagging in order to deal with unbalanced datasets, and on the automatic selection of difficult examples. These techniques allow us to reach 91.4, 46.5, 47.8, 85.0 as F1-scores for Tasks 1 to 4.
Anthology ID:
W18-5913
Volume:
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
50–51
Language:
URL:
https://www.aclweb.org/anthology/W18-5913
DOI:
10.18653/v1/W18-5913
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5913.pdf