BERT based Transformers lead the way in Extraction of Health Information from Social Media
Sidharth Ramesh, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma
Abstract
This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2021 shared tasks. We participated in 2 tasks: (1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms (Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific pre-processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the median score across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.- Anthology ID:
- 2021.smm4h-1.5
- Volume:
- Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
- Month:
- June
- Year:
- 2021
- Address:
- Mexico City, Mexico
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 33–38
- Language:
- URL:
- https://aclanthology.org/2021.smm4h-1.5
- DOI:
- 10.18653/v1/2021.smm4h-1.5
- Cite (ACL):
- Sidharth Ramesh, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, and Ujjwal Verma. 2021. BERT based Transformers lead the way in Extraction of Health Information from Social Media. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 33–38, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- BERT based Transformers lead the way in Extraction of Health Information from Social Media (Ramesh et al., SMM4H 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.smm4h-1.5.pdf
- Code
- sahilkhose/SMM4H21
- Data
- WebText