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        <title>Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection</title>
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        <namePart type="given">Adith</namePart>
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        <namePart type="given">Aditya</namePart>
        <namePart type="family">Joshi</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Cecile</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
%A Iyer, Adith
%A Joshi, Aditya
%A Karimi, Sarvnaz
%A Sparks, Ross
%A Paris, Cecile
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F iyer-etal-2019-figurative
%X 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.
%R 10.18653/v1/P19-1108
%U https://aclanthology.org/P19-1108
%U https://doi.org/10.18653/v1/P19-1108
%P 1142-1147

Markdown (Informal)

[Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection](https://aclanthology.org/P19-1108) (Iyer et al., ACL 2019)

• Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection (Iyer et al., ACL 2019)

ACL

• Adith Iyer, Aditya Joshi, Sarvnaz Karimi, Ross Sparks, and Cecile Paris. 2019. Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1142–1147, Florence, Italy. Association for Computational Linguistics.