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

Adith Iyer, Aditya Joshi, Sarvnaz Karimi, Ross Sparks, Cecile Paris


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.
Anthology ID:
P19-1108
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1142–1147
URL:
https://www.aclweb.org/anthology/P19-1108
DOI:
10.18653/v1/P19-1108
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PDF:
https://www.aclweb.org/anthology/P19-1108.pdf