Transfer Learning for Health-related Twitter Data

Anne Dirkson, Suzan Verberne


Abstract
Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data. This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task. Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets. The code and fine-tuned models are publicly available.
Anthology ID:
W19-3212
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:
89–92
Language:
URL:
https://www.aclweb.org/anthology/W19-3212
DOI:
10.18653/v1/W19-3212
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3212.pdf