Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
This work addresses a specific problem in natural language processing for health informatics by improving detection accuracy in personal health mentions, though it is incremental as it builds on existing methods.
The paper tackled errors in personal health mention detection caused by figurative usage of symptom words by combining figurative usage detection with CNN-based detection, resulting in an average improvement of 2.21% F-score using a feature augmentation approach.
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.