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A practical primer on processing semantic property norm data.

Erin M Buchanan1, Simon De Deyne2, Maria Montefinese3,4

  • 1Harrisburg University of Science and Technology, 326 Market St., Harrisburg, PA, 17101, USA. ebuchanan@harrisburgu.edu.

Cognitive Processing
|November 27, 2019
PubMed
Summary
This summary is machine-generated.

This study offers a practical guide for collecting and processing semantic property norms, crucial for cognitive science research. It details methods to improve data quality and comparability across studies.

Keywords:
Property norm taskSemanticTutorial

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Area of Science:

  • Cognitive Psychology
  • Computational Linguistics
  • Psycholinguistics

Background:

  • Semantic property listing is vital for creating semantic property norms used in cognitive modeling and stimuli creation.
  • Methodological choices in property listing tasks significantly impact the quality and nature of derived semantic measures.
  • Existing research often overlooks the methodological details of property listing, hindering cross-study comparability.

Purpose of the Study:

  • To provide a practical primer on collecting and processing semantic property norms.
  • To discuss methods for eliciting semantic properties and deriving meaningful representations.
  • To propose a transparent processing pipeline for enhanced comparability across studies.

Main Methods:

  • Discussing the influence of instructions and test context on property elicitation.
  • Detailing property preprocessing techniques such as lemmatization.
  • Exploring property weighting and relationship encoding using ontologies.
  • Demonstrating a processing pipeline with intrinsic and extrinsic measures.

Main Results:

  • The proposed pipeline enhances transparency and comparability of semantic property norms.
  • Methodological choices demonstrably impact measures of reliability, property count, categorization, and semantic similarity.
  • The primer offers solutions to long-standing issues in semantic norm generation.

Conclusions:

  • Standardizing the collection and processing of semantic property norms is essential for robust cognitive research.
  • The presented methods and pipeline facilitate the development of high-quality, comparable semantic datasets.
  • This work empowers researchers to overcome previous limitations in creating semantic property norms.