This paper describes our system for addressing SMM4H 2023 Shared Task 2 on "Classification of sentiment associated with therapies (aspect-oriented)". In our work, we adopt an approach based on Natural language inference (NLI) to formulate this task as a sentence pair classification problem, and train transformer models to predict sentiment associated with a therapy on a given text. Our best model achieved 75.22\% F1-score which was 11\% (4\%) more than the mean (median) score of all teams' submissions.
@article{arxiv.2312.03737,
title = {A Generic NLI approach for Classification of Sentiment Associated with Therapies},
author = {Rajaraman Kanagasabai and Anitha Veeramani},
journal= {arXiv preprint arXiv:2312.03737},
year = {2023}
}
Comments
Accepted in Workshop on Social Media Mining for Health 2023 (#SMM4H)