tmn at #SMM4H 2023: Comparing Text Preprocessing Techniques for Detecting Tweets Self-reporting a COVID-19 Diagnosis

• URL: http://arxiv.org/abs/2311.00732v1
• Date: Wed, 1 Nov 2023 07:41:23 GMT
• Title: tmn at #SMM4H 2023: Comparing Text Preprocessing Techniques for Detecting Tweets Self-reporting a COVID-19 Diagnosis
• Authors: Anna Glazkova
• Abstract summary: The paper describes a system developed for Task 1 at SMM4H 2023. The goal of the task is to automatically distinguish tweets that self-report a COVID-19 diagnosis from those that do not.
• Score: 1.8492669447784602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper describes a system developed for Task 1 at SMM4H 2023. The goal of the task is to automatically distinguish tweets that self-report a COVID-19 diagnosis (for example, a positive test, clinical diagnosis, or hospitalization) from those that do not. We investigate the use of different techniques for preprocessing tweets using four transformer-based models. The ensemble of fine-tuned language models obtained an F1-score of 84.5%, which is 4.1% higher than the average value.

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