# Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection

arXiv: https://arxiv.org/abs/1906.05466 · HTML: https://mlsanity.com/paper/1906.05466/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets](https://mlsanity.com/paper/2204.06337/) (2022)
- [Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training](https://mlsanity.com/paper/2203.01895/) (2022)
- [A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content](https://mlsanity.com/paper/2310.19057/) (2023)
- [Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning](https://mlsanity.com/paper/2405.01597/) (2024)
- [Incorporating Emotions into Health Mention Classification Task on Social Media](https://mlsanity.com/paper/2212.05039/) (2022)
- [Multi-task Learning for Personal Health Mention Detection on Social Media](https://mlsanity.com/paper/2212.05147/) (2022)
- [Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning](https://mlsanity.com/paper/2504.21685/) (2025)

## 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.