Abstract
This paper details a system designed for Social Media Mining for Health Applications (SMM4H) Shared Task 2020. We specifically describe the systems designed to solve task 2: Automatic classification of multilingual tweets that report adverse effects, and task 3: Automatic extraction and normalization of adverse effects in English tweets. Fine tuning RoBERTa large for classifying English tweets enables us to achieve a F1 score of 56%, which is an increase of +10% compared to the average F1 score for all the submissions. Using BERT based NER and question answering, we are able to achieve a F1 score of 57.6% for extracting adverse reaction mentions from tweets, which is an increase of +1.2% compared to the average F1 score for all the submissions.- Anthology ID:
- 2020.smm4h-1.16
- 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:
- 104–109
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2020.smm4h-1.16/
- DOI:
- Cite (ACL):
- Sougata Saha, Souvik Das, Prashi Khurana, and Rohini Srihari. 2020. Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 104–109, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Autobots Ensemble: Identifying and Extracting Adverse Drug Reaction from Tweets Using Transformer Based Pipelines (Saha et al., SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2020.smm4h-1.16.pdf