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Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
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In this paper, the authors combine a state-of-the-art figurative usage detection with CNN-based personal health mention detection for predicting whether or not a given sentence is a report of a health condition.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.read more
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IMPLI: Investigating NLI Models’ Performance on Figurative Language
TL;DR: IMPLI is introduced, an English dataset consisting of paired sentences spanning idioms and metaphors and it is shown that while NLI models can reliably detect entailment relationship between figurative phrases with their literal counterparts, they perform poorly on similarly structured examples where pairs are designed to be non-entailing.
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COVID-19 personal health mention detection from tweets using dual convolutional neural network
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Proceedings ArticleDOI
Identification of Disease or Symptom terms in Reddit to Improve Health Mention Classification
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Performance Comparison of Transformer-Based Models on Twitter Health Mention Classification
TL;DR: In this paper , the authors compared nine widely used transformer methods and compared their performance on the personal health mention classification of tweet data, and analyzed the impact of model size on the classification task and provided a brief interpretation of the classification decision made by the best performing classifier.
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