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)
Venues:
COLING | SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–133
Language:
URL:
https://aclanthology.org/2020.smm4h-1.23
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/2020.smm4h-1.23.pdf