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Compounding as Abstract Operation in Semantic Space: Investigating relational effects through a large-scale,

Marco Marelli1, Christina L Gagné2, Thomas L Spalding2

  • 1University of Milano-Bicocca, Department of Psychology, Piazza dell'Ateneo Nuovo 1, 20126 Milano, Italy.

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|June 6, 2017
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Summary

This study introduces the CAOSS model, a computational system for understanding novel word compounds. It shows how relational information in language usage can be learned and applied to process new word combinations.

Keywords:
Compound wordsConceptual combinationDistributional semanticsNovel compoundsRelational information

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Area of Science:

  • Computational linguistics
  • Cognitive science
  • Natural Language Processing

Background:

  • Compounding is a key word-formation process in many languages.
  • Native speakers readily understand novel compounds formed by juxtaposing words.
  • Existing models may not fully capture the semantic processing of novel compounds.

Purpose of the Study:

  • To propose a large-scale, data-driven computational system for compound semantic processing.
  • To model how novel compounds are understood based on distributional semantics.
  • To validate the model against human behavioral data on compound processing.

Main Methods:

  • Developed the CAOSS (Compounding as Abstract Operation in Semantic Space) model.
  • Represented word meanings as vectors based on lexical co-occurrences.
  • Computed compound representations using matrices for constituent roles (modifier vs. head).
  • Induced matrices from language usage examples.

Main Results:

  • CAOSS model predictions aligned with behavioral results on novel compound processing.
  • The model captured relational priming and dominance effects.
  • Simulations showed that relational information is implicitly present in language usage.

Conclusions:

  • Relational information can be learned from language experience and automatically applied to new word combinations.
  • The CAOSS model demonstrates flexibility in emulating human compound processing.
  • Relational effects may arise from nuanced distributional pattern operations.