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Automatic Scoring of Semantic Fluency.

Najoung Kim1, Jung-Ho Kim1, Maria K Wolters2

  • 1School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.

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|June 4, 2019
PubMed
Summary
This summary is machine-generated.

A new Wikipedia-based method, Backlink-VSM, automatically scores semantic fluency tasks. This approach accurately reflects manual scoring and distinguishes age-related cognitive differences.

Keywords:
category fluency testexecutive functionrelation extractionsemantic fluencysemantic memoryverbal fluencyword embeddings

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Area of Science:

  • Neuropsychology
  • Computational Linguistics
  • Cognitive Science

Background:

  • Semantic fluency tasks assess executive functions and semantic memory access.
  • Traditional scoring relies on word counts, but manual methods offer deeper insights via semantic clustering and switching.
  • Existing automatic scoring methods often lack theoretical grounding or require extensive linguistic resources.

Purpose of the Study:

  • To introduce Backlink-VSM, a novel, adaptable automatic scoring method for semantic fluency.
  • To demonstrate Backlink-VSM's ability to approximate manual scoring metrics and account for cultural nuances.
  • To validate Backlink-VSM's performance against manual scoring and existing computational models.

Main Methods:

  • Developed Backlink-VSM, integrating Wikipedia's hyperlink structure (Backlink model) with a vector space model (VSM) for semantic proximity.
  • Applied the method to English and Korean semantic fluency data (animals, fruits).
  • Compared Backlink-VSM's output with manual annotations and a WordNet-based model for accuracy in differentiating age groups.

Main Results:

  • Backlink-VSM measures showed strong correlations with manual annotations in both English and Korean.
  • The method successfully replicated the finding of fewer switches in older adults compared to younger adults.
  • Backlink-VSM outperformed manual scoring and a WordNet-based model in classifying participants by age group.

Conclusions:

  • Backlink-VSM provides a robust, language- and culture-adaptable automatic scoring solution for semantic fluency.
  • The method offers a clear link to established manual scoring metrics like clustering and switching.
  • Backlink-VSM demonstrates superior performance in capturing age-related cognitive differences in semantic processing.