Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models (N18-2)
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| Challenge: | Current diagnoses often involve lengthy medical evaluations. |
| Approach: | They apply neural models based on CNNs, LSTM-RNNs, and their combination to classify AD and control language samples. |
| Outcome: | The proposed model achieves independent benchmark accuracy for the AD classification task. |
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Enriching Neural Models with Targeted Features for Dementia Detection (P19-2)
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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. |
| Approach: | They propose a neural model that takes into account both long language samples and hand-crafted linguistic features to distinguish between dementia affected and healthy patients. |
| Outcome: | The proposed model achieves an F1 score of 0.929 on the DementiaBank dataset and the state-of-the-art on the dataset. |
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models (2022.acl-long)
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| Challenge: | Existing methods for fine-tuning large numbers of model parameters have shown impressive performance on the task of discriminating between language produced by cognitively healthy individuals and those with Alzheimer’s disease (AD). |
| Approach: | They propose to use a Transformer DL model pre-trained on general English text to combine an artificially degraded version of itself with a model that generalizes well to spontaneous conversations. |
| Outcome: | The proposed method generalizes well to spontaneous conversations and generates text with characteristics associated with AD, demonstrating the induction of dementia-related linguistic anomalies. |
Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling (N19-1)
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Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, Dimitrios Kokkinakis
| Challenge: | Existing work on speech and language models has been limited by the size of available datasets. |
| Approach: | They propose to augment a small French dataset with a much larger English dataset to augment the language model to model the order in which information units are produced by dementia patients and controls. |
| Outcome: | The proposed model improves classification performance in English and French separately. |
LoSST-AD: A Longitudinal Corpus for Tracking Alzheimer’s Disease Related Changes in Spontaneous Speech (2024.lrec-main)
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| Challenge: | Language-based biomarkers have shown promising results in differentiating those with Alzheimer’s disease (AD) diagnosis from healthy individuals, but the earliest changes in language are thought to start years or even decades before the diagnosis. |
| Approach: | They propose to use transcripts of public interviews with 20 famous figures to track language change over several decades to validate their corpus. |
| Outcome: | The proposed corpus can provide a valuable starting point for the development of early detection tools and enhance our understanding of how AD affects language over time. |
A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer’s Type (2020.acl-main)
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| Challenge: | Recent studies show that cognitive manifestations of future dementia may appear as early as 18 years prior to clinical diagnosis . lack of clear diagnosis and prognosis, possibly for an Alzheimer's type, is a major limitation of current methods for identifying dementia-specific cognitive markers. |
| Approach: | They propose to interrogate neural LMs trained on participants with and without dementia by manipulating lexical frequency. |
| Outcome: | The proposed model improves upon the current state-of-the-art for models trained on transcripts of speech produced by healthy participants and those with dementia. |
Adversarial Text Generation using Large Language Models for Dementia Detection (2024.emnlp-main)
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| Challenge: | Large language models excel in text classification tasks, but they do not perform well with picture description. |
| Approach: | They propose an interpretable classification approach by Adversarial Text Generation (ATG) that could relate dementia detection with other tasks. |
| Outcome: | The proposed approach achieves 85% accuracy, >10% improvement over the previous methods. |
The Influence of Automatic Speech Recognition on Linguistic Features and Automatic Alzheimer’s Disease Detection from Spontaneous Speech (2024.lrec-main)
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| Challenge: | Existing biomarkers for AD diagnosis can only be applied to relatively small sample sizes due to limited availability, excessive costs and invasive nature. |
| Approach: | They compare automatic speech recognition systems in terms of Word Error Rate (WER) using a publicly available benchmark dataset of speech recordings of AD patients and controls. |
| Outcome: | The proposed method improves classification performance by replacing manual transcriptions with ASR output. |
Transforming Brainwaves into Language: EEG Microstates Meet Text Embedding Models for Dementia Detection (2025.acl-srw)
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| Challenge: | Dementia is recognised as the seventh leading cause of mortality globally and plays a major role in increasing disability and dependence among older adults. |
| Approach: | They propose to represent electroencephalography microstates as symbolic, language-like sequences and use text embedding and time-series deep learning models for classification. |
| Outcome: | The proposed method achieves a high accuracy of 94.31% on 1001 EEG data from multiple countries and eliminates fixed configurations and costly/invasive modalities. |
Linguistic Features Extracted by GPT-4 Improve Alzheimer’s Disease Detection based on Spontaneous Speech (2025.coling-main)
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| Challenge: | Large language models (LLMs) have enabled powerful new possibilities for semantic text analysis. |
| Approach: | They leverage GPT-4 to extract five semantic features from transcripts of spontaneous patient speech. |
| Outcome: | The proposed model significantly improves detection of AD in manually transcribed and automatically generated transcripts. |
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 . |
| Approach: | They propose a method that uses LLMs to extract high-level, expert-guided features . human perception of dementia is inconsistent and relies on a narrow set of cues, they say . |
| Outcome: | The proposed method analyzes picture descriptions to assess whether they were produced by non-experts or by nonexperts. |