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Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics.

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This study introduces a computational model for understanding how word parts (morphemes) combine to create new meanings. The model uses vector representations of word meanings and matrix transformations for affixes, explaining word formation and semantic transparency.

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Area of Science:

  • Computational linguistics
  • Cognitive science
  • Psycholinguistics

Background:

  • Word meanings are often represented using distributional semantics, where word vectors capture co-occurrence patterns in large text corpora.
  • Morpheme combination is crucial for word formation and semantic processing, yet computational models are limited.

Purpose of the Study:

  • To propose a computational model for morpheme combination at the meaning level.
  • To explain the human ability to generate novel words with novel meanings.
  • To provide a framework for understanding semantic transparency in word processing.

Main Methods:

  • Modeling affixes as functions (matrices) that transform stem vectors.
  • Utilizing distributional semantics and vector space models for word meaning representation.
  • Testing model predictions against human semantic intuitions, lexical decision times, and morphological priming.

Main Results:

  • The model successfully accounts for the generation of novel word meanings through compositional procedures.
  • It predicts semantic intuitions about novel derived words.
  • Model predictions align with empirical data on semantic transparency, including response times and priming effects.

Conclusions:

  • A data-driven, theoretically sound, and empirically supported computational model for morpheme combination at the meaning level has been developed.
  • The model offers a new framework for semantic transparency, linking it to the ease and strength of affixal transformations.
  • This work opens new research avenues in computational and cognitive semantic processing.