Abstract
This paper describes our approach for detecting adverse drug effect mentions on Twitter as part of the Social Media Mining for Health Applications (SMM4H) 2020, Shared Task 2. Our approach utilizes multilingual sentence embeddings (sentence-BERT) for representing tweets and Bayesian hyperparameter optimization of sample weighting parameter for counterbalancing high class imbalance.- Anthology ID:
- 2020.smm4h-1.30
- Volume:
- Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Graciela Gonzalez-Hernandez, Ari Z. Klein, Ivan Flores, Davy Weissenbacher, Arjun Magge, Karen O'Connor, Abeed Sarker, Anne-Lyse Minard, Elena Tutubalina, Zulfat Miftahutdinov, Ilseyar Alimova
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 161–164
- Language:
- URL:
- https://aclanthology.org/2020.smm4h-1.30
- DOI:
- Cite (ACL):
- Oguzhan Gencoglu. 2020. Sentence Transformers and Bayesian Optimization for Adverse Drug Effect Detection from Twitter. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 161–164, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Sentence Transformers and Bayesian Optimization for Adverse Drug Effect Detection from Twitter (Gencoglu, SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.smm4h-1.30.pdf