Challenge: Past work in personal health mention detection uses classification-based methods with human-engineered features or word embedding-based features.
Approach: They propose to combine a pipeline-based and a feature augmentation-based approach to combine personal health mention detection with figurative usage detection to improve the accuracy of the prediction.
Outcome: The proposed method improves the F-score of personal health mention detection by 2.21% over the pipeline-based approach and feature augmentation-based approaches.

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Challenge: Existing studies on social media for deriving mental health status of users focus on the depression detection task.
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ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure (2024.lrec-main)

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Challenge: Existing methods for identifying metaphoric expressions in text relied on manual effort to identify the basic and contextual meanings of words.
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Extracting Symptoms and their Status from Clinical Conversations (P19-1)

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Challenge: Existing models for extracting symptoms from clinical conversations are inherently difficult.
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Mining Health-related Cause-Effect Statements with High Precision at Large Scale (2022.coling-1)

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Challenge: Existing methods for assessing the health relatedness of phrases and sentences are slower and less effective than state-of-the-art medical entity linkers.
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Challenge: Existing methods to identify metaphors use contextual information extracted by transformers for classifications directly.
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Metaphorical Polysemy Detection: Conventional Metaphor Meets Word Sense Disambiguation (2022.coling-1)

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Challenge: Linguists distinguish between novel and conventional metaphors, a distinction which the metaphor detection task in NLP does not take into account.
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Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings (2022.coling-1)

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Challenge: Existing black-box-like deep learning methods for depression detection focus on improving classification performance, but it is impossible to explain and interpret those models that rely on state-of-the-art (SOTA) deep learning techniques.
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Neural Metaphor Detection in Context (D18-1)

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Challenge: Existing models focus on limited forms of linguistic context, such as unigrams.
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