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Semantic transparency effects in German compounds: A large dataset and multiple-task investigation.

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This study introduces a dataset of semantic transparency measures for German compound words. A multi-dimensional approach reveals task-specific effects, enhancing our understanding of word meaning composition.

Keywords:
Compound wordsDistributional semanticsEye trackingLexical decisionSemantic transparency

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Area of Science:

  • Psycholinguistics
  • Computational Linguistics
  • Cognitive Science

Background:

  • Compound words' meaning can be transparent or opaque.
  • The degree of semantic transparency varies significantly.
  • Existing measures often fail to capture this complexity.

Purpose of the Study:

  • To create a comprehensive dataset of semantic transparency measures for German compounds.
  • To develop and validate a multi-dimensional approach to semantic transparency.
  • To provide a resource for future research on word meaning.

Main Methods:

  • Utilized a computational semantic model based on distributional semantics.
  • Generated compositional and relatedness-based transparency measures.
  • Validated measures through four behavioral experiments (ratings, lexical decision, eye-tracking).

Main Results:

  • Different semantic effects were observed across various behavioral tasks.
  • A multi-dimensional approach is crucial for capturing nuanced semantic effects.
  • The dataset includes measures for over 1800 German compounds and additional large sets.

Conclusions:

  • Semantic transparency is a complex, multi-faceted phenomenon.
  • The developed dataset and model offer a robust tool for linguistic research.
  • Findings highlight the importance of task-specific analysis in psycholinguistics.