Detecting cognitive impairments by agreeing on interpretations of linguistic features (N19-1)
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| Challenge: | Linguistic features have shown promising applications for detecting cognitive impairments. |
| Approach: | They propose a framework to classify after reaching agreements between modalities by using linguistic features to divide linguistic subsets into subset and let neural networks learn low-dimensional representations that agree with each other. |
| Outcome: | The proposed framework outperforms existing classifiers using all of the 413 linguistic features. |
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| Challenge: | In the United States, adults over 65 are expected to comprise one-fifth of the population by 2030, and a larger proportion of the . population than those under 18 by 2035. |
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Badr AlKhamissi, Greta Tuckute, Yingtian Tang, Taha Osama A Binhuraib, Antoine Bosselut, Martin Schrimpf
| Challenge: | Large language models exhibit remarkable similarity to neural activity in the human language network, but their properties remain unclear. |
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Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness (2025.findings-emnlp)
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| Challenge: | a new study examines how dementia is perceived by non-experts . human perception of dementia is inconsistent and relies on a narrow set of cues compared to LLMs based on broader clinical patterns . |
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CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals (2021.acl-long)
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| Challenge: | Existing studies integrate word embeddings with cognitive features into neural models of natural language processing (NLP) but there are some issues in the use of cognitive features in NLP. |
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Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment (2021.acl-long)
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| Challenge: | Existing deep learning models for automatic readability assessment discard linguistic features traditionally used for the task. |
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Feature Interactions Reveal Linguistic Structure in Language Models (2023.findings-acl)
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| Challenge: | Existing features attribution methods for post-hoc interpretability ignore the existence of interactions between the effects of features on the prediction. |
| Approach: | They propose a grey box method to train models to perfection on a formal language classification task using PCFGs. |
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Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models (N18-2)
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| Challenge: | Existing methods to identify key neurons for interpretability of multi-modal large language models are unclear. |
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The Automatic Extraction of Linguistic Biomarkers as a Viable Solution for the Early Diagnosis of Mental Disorders (2022.lrec-1)
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| Challenge: | Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. |
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Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction (2020.acl-main)
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| Challenge: | Neural relation extraction models capture linguistic and semantic properties of the input, a recent study shows. |
| Approach: | They introduce 14 probing tasks targeting linguistic properties relevant to RE . they add contextualized word representations to enhance probing performance . |
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