Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
Abstract
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.
Keywords
Cite
@article{arxiv.1906.05466,
title = {Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection},
author = {Adith Iyer and Aditya Joshi and Sarvnaz Karimi and Ross Sparks and Cecile Paris},
journal= {arXiv preprint arXiv:1906.05466},
year = {2019}
}
Comments
To appear at the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) (The second version updates the name of a cited paper. A detailed note from the cited author is here : https://github.com/commonsense/conceptnet5/wiki/Citation-complications )