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Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces.

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|March 19, 2016
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Summary

This study introduces a new method for predicting the acceptability of novel adjective-noun phrases using compositional distributional semantics. The key finding is that how an adjective changes a noun's meaning significantly impacts phrase acceptability.

Keywords:
CompositionalityDistributional modelsMeaning representationSemantic devianceSemantic spaces

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Natural Language Processing

Background:

  • Human language understanding relies on semantic compositionality.
  • Predicting the acceptability of novel phrases is a challenge in linguistics and cognitive science.
  • Compositional distributional semantics offers a computational approach to modeling meaning.

Purpose of the Study:

  • To introduce a large dataset of human judgments on novel adjective-noun phrases.
  • To test a compositional distributional semantic approach for predicting semantic deviance.
  • To identify factors influencing the acceptability of novel linguistic expressions.

Main Methods:

  • Collected human judgments on the acceptability of novel adjective-noun phrases.
  • Utilized compositional distributional semantic methods to derive phrase representations.
  • Developed and tested measures based on distributional representations of words and phrases.

Main Results:

  • Distributional measures significantly predicted phrase acceptability.
  • The degree to which an adjective alters a noun's distributional representation was the most impactful factor.
  • The proposed method outperformed traditional measures in predicting acceptability.

Conclusions:

  • Compositional distributional semantics can effectively model human judgments of semantic deviance.
  • The alteration of distributional representations is a key predictor of phrase acceptability.
  • This work provides a quantitative approach to understanding novel linguistic expression acceptability.