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

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arxiv 1906.05466 v2 pith:URD4CZA4 submitted 2019-06-13 cs.CL cs.IR

Adith Iyer , Aditya Joshi , Sarvnaz Karimi , Ross Sparks , Cecile Paris This is my paper · ORCID
classification cs.CLcs.IR
keywords detectionhealthfigurativementionpersonalusageapproachaugmentation-based
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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.

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  1. Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning

    cs.CL 2025-04 reject novelty 4.0 of 10

    Applying prompt tuning and POS tagger features to health mention classification yields small F1 improvements over plain fine-tuning, but the paper does not compare with actual state-of-the-art systems.

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