Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
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Abstract (arXiv)
Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work mentions errors in this prediction when symptom words, i.e. names of symptoms of interest, are used in a figurative sense. Therefore, we combine a state-of-the-art figurative usage detection with CNN-based personal health mention detection. To do so, we present two methods: a pipeline-based approach and a feature augmentation-based approach. The introduction of figurative usage detection results in an average improvement of 2.21% F-score of personal health mention detection, in the case of the feature augmentation-based approach. This paper demonstrates the promise of using figurative usage detection to improve personal health mention detection.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2019.
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Citing papers per year: 2022: 4; 2023: 2; 2024: 1; 2025: 1.
8 papers.
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- 2023A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Contentcited by 1
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- 2022Multi-task Learning for Personal Health Mention Detection on Social Mediacited by 0
- 2025Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuningcited by 0
- 2025Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning30 Apr 2025
- 2024Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning30 Apr 2024
- 2023A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content29 Oct 2023
- 2023Can Large Language Models Transform Computational Social Science?12 Apr 2023
- 2022Multi-task Learning for Personal Health Mention Detection on Social Media9 Dec 2022
- 2022Incorporating Emotions into Health Mention Classification Task on Social Media9 Dec 2022
- 2022A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets13 Apr 2022
- 2022Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training3 Mar 2022