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A Generic NLI approach for Classification of Sentiment Associated with Therapies
November 28, 2023 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Rajaraman Kanagasabai, Anitha Veeramani
arXiv ID
2312.03737
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
0
Venue
arXiv.org
Last Checked
6 months ago
Abstract
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.
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