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

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1142–1147 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 1142 Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection Adith Iyer♠♣, Aditya Joshi♠, Sarvnaz Karimi♠, Ross Sparks♠, C´ecile Paris♠ ♠CSIRO Data61, Sydney, Australia ♣University of Queensland, Brisbane, Australia adith.iyer@uq.net.au, {firstname.lastname}@csiro.au Abstract Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work men- tions errors in this prediction when symptom words, i.e., names of symptoms of interest, are used in a figurative sense. Therefore, we com- bine a state-of-the-art figurative usage detec- tion with CNN-based personal health mention detection. To do so, we present two meth- ods: a pipeline-based approach and a feature augmentation-based approach. The introduc- tion of figurative usage detection results in an average improvement of 2.21% F-score of per- sonal 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. 1 Introduction The World Health Organisation places importance on gathering intelligence about epidemics to be able to effectively respond to them (World Health Organisation, 2019). Natural language process- ing (NLP) techniques have been applied to social media datasets for epidemic intelligence (Charles- Smith et al., 2015). An important classifica- tion task in this area is personal health men- tion detection: to detect whether or not a text contains a personal health mention (PHM). A PHM is a report that either the author or some- one they know is experiencing a health condi- tion or a symptom (Lamb et al., 2013). For ex- ample, the sentence ‘I have been coughing since morning’ is a PHM, while ‘Having a cough for three weeks or more could be a sign of cancer’ is not. The former reports that the author has a cough while, in the latter, the author provides information about coughs in general. Past work in PHM detection uses classification-based ap- proaches with human-engineered features (Lamb et al., 2013; Yin et al., 2015) or word embedding- based features (Karisani and Agichtein, 2018). However, consider the quote ‘When Paris sneezes, Europe catches cold’ attributed to Klemens von Metternich1. The quote contains names of symp- toms (referred to as ‘symptom words’ hereafter) ‘sneezes’ and ‘cold’. However, it is not a PHM, since the symptom words are used in a figurative sense. Since several epidemic intelligence tools based on social media rely on counts of keyword occurrences (Charles-Smith et al., 2015), figura- tive sentences may introduce errors. Figurative us- age has been quoted as a source of error in past work (Jimeno Yepes et al., 2015; Karisani and Agichtein, 2018). In this paper, we deal with the question: Does personal health mention detection benefit from knowing if symptom words in a text were used in a literal or figura- tive sense? To address the question, we use a state-of- the-art approach that …

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