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
- Editors:
- Davy Weissenbacher, Graciela Gonzalez-Hernandez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–92
- Language:
- URL:
- https://aclanthology.org/W19-3212
- DOI:
- 10.18653/v1/W19-3212
- Cite (ACL):
- Anne Dirkson and Suzan Verberne. 2019. Transfer Learning for Health-related Twitter Data. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 89–92, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Transfer Learning for Health-related Twitter Data (Dirkson & Verberne, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/W19-3212.pdf
- Code
- AnneDirkson/SharedTaskSMM4H2019