Summary of the paper

Title Language Modelling for the Clinical Semantic Verbal Fluency Task
Authors Nicklas Linz and Johannes Tröger
Abstract Semantic Verbal Fluency (SVF) tests are common neuropsychological tasks, in which patients are asked to name as many words belonging to a semantic category as they can in 60 seconds. These tests are sensitive to even early forms of dementia caused by e.g. Alzheimer’s disease. Performance is usually measured as the total number of correct responses. Clinical research has shown that not only the raw count, but also production strategy is a relevant clinical marker. We employed language modelling (LM) as a natural technique to model production in this task. Comparing different LMs, we show that perplexity of a persons SVF production predicts dementia well (F1 = 0:83). Demented patients show significantly lower perplexity, thus are more predictable. Persons in advanced stages of dementia differ in predictability of word choice and production strategy - people in early stages only in predictability of production strategy.
Topics Alzheimer’S Disease, Semantic Verbal Fluency, Dementia, Machine Learning, Language Modelling
Full paper Language Modelling for the Clinical Semantic Verbal Fluency Task
Bibtex @InProceedings{LINZ18.4,
  author = {Nicklas Linz and Johannes Tröger},
  title = {Language Modelling for the Clinical Semantic Verbal Fluency Task},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {may},
  date = {7-12},
  location = {Miyazaki, Japan},
  editor = {Dimitrios Kokkinakis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {979-10-95546-26-9},
  language = {english}
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