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Semantic coherence markers: The contribution of perplexity metrics
Davide Colla1, Matteo Delsanto1, Marco Agosto2
1University of Turin, Computer Science Department, Italy.
Artificial Intelligence in Medicine
|December 3, 2022
Summary
Language models can detect mental health conditions by analyzing text. Perplexity scores accurately differentiate between healthy individuals and those with Alzheimer
Area of Science:
- Computational linguistics
- Psychiatric diagnostics
- Artificial intelligence in healthcare
Background:
- Early detection of mental disturbances and psychotic disorders is a significant challenge.
- Language models offer a novel approach to analyzing linguistic patterns for diagnostic support.
Purpose of the Study:
- To investigate the utility of language models and perplexity scores for analyzing individual language characteristics.
- To determine if perplexity can discriminate between healthy subjects and those with Alzheimer Disease (AD).
Main Methods:
- Utilized various language models, including N-grams (2- to 5-grams) and GPT-2.
- Assessed perplexity scores across different linguistic registers and subject groups.
- Conducted experiments to differentiate between transcripts of healthy subjects and AD patients.
Main Results:
- Perplexity scores demonstrated stability and consistency across different language models and individual subjects.
- Scores were sensitive to variations in linguistic registers within the same speaker.
- Models achieved perfect accuracy (1.00) in classifying subjects with and without Alzheimer Disease.
Conclusions:
- Perplexity is a reliable metric for analyzing individual language and detecting linguistic anomalies.
- Perplexity analysis using language models shows significant potential for supporting the early diagnosis of mental disorders like AD.
Keywords:
Automatic language analysisDiagnosis of dementiaEarly diagnosisLanguage modelsMental and cognitive disordersPerplexityMore Related Videos
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