A Generic NLI approach for Classification of Sentiment Associated with Therapies

A Generic NLI approach for Classification of Sentiment Associated with Therapies

Social Media Mining for Health (Workshop)

arxiv.org/abs/2312.03737v1

https://arxiv.org/pdf/2312.03737

Rajaraman Kanagasabai, Anitha Veeramani

data analytic

28 November 2023

arXiv:2312.03737

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

Publisher Copyright

This research is initiated from the project TIDE (EC-2021-046) "Improving infectious disease forecasting through social media data"

https://oar.a-star.edu.sg/communities-collections/articles/21031

2312.03737

Institute for Infocomm Research