2019/06/13 by Adith Iyer, Iyer, Adith, Aditya Joshi +7 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.1906.05466
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 )
arxiv created 2019/07/04 · arxiv updated 2019/07/05
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.