Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
Adith Iyer,Aditya Joshi,Sarvnaz Karimi,Ross Sparks,Cecile Paris +4 more
- pp 1142-1147
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TLDR
The promise of using figurative usage detection to improve personal health mention detection is demonstrated by presenting two methods: a pipeline-based approach and a feature augmentation- based approach.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
Citations
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Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework
Shweta Yadav,Jainish Chauhan,Joy Prakash Sain,Krishnaprasad Thirunarayan,Amit Sheth,Jeremiah A. Schumm +5 more
TL;DR: This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of the Patient Health Questionnaire-9 that is routinely used in clinical practice.
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Survey of Text-based Epidemic Intelligence: A Computational Linguistics Perspective
TL;DR: This survey discusses approaches for epidemic intelligence that use textual datasets, referring to it as “text-based epidemic intelligence,” view past work in terms of two broad categories: health mention classification and health event detection.
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Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter
TL;DR: This work presents a method that utilises sentiment information to improve health mention classification, and outperforms current SOTA approaches in detecting both health-related and figurative tweets that mention disease words.
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COVID-19 personal health mention detection from tweets using dual convolutional neural network
Linkai Luo,Yue Wang,Hailiang Liu +2 more
TL;DR: Wang et al. as mentioned in this paper built a COVID-19 PHM dataset containing more than 11,000 annotated tweets, and proposed a dual convolutional neural network (CNN) framework using this dataset.
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Improving Personal Health Mention Detection on Twitter Using Permutation Based Word Representation Learning
TL;DR: In this article, a permutation-based contextual word representation was proposed for health mention detection, which captures the context of disease words efficiently, in the given piece of text, and hence improves the performance of the classifier.
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