Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees
Lung-Hao Lee, Po-Han Chen, Hao-Chuan Kao, Ting-Chun Hung, Po-Lei Lee, Kuo-Kai Shyu
Abstract
This study describes our proposed model design for the SMM4H 2020 Task 1. We fine-tune ELECTRA transformers using our trained SVM filter for data augmentation, along with decision trees to detect medication mentions in tweets. Our best F1-score of 0.7578 exceeded the mean score 0.6646 of all 15 submitting teams.- Anthology ID:
- 2020.smm4h-1.23
- Volume:
- Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 131–133
- Language:
- URL:
- https://aclanthology.org/2020.smm4h-1.23
- DOI:
- Cite (ACL):
- Lung-Hao Lee, Po-Han Chen, Hao-Chuan Kao, Ting-Chun Hung, Po-Lei Lee, and Kuo-Kai Shyu. 2020. Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 131–133, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Medication Mention Detection in Tweets Using ELECTRA Transformers and Decision Trees (Lee et al., SMM4H 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.smm4h-1.23.pdf