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Investigating the Extent to which Distributional Semantic Models Capture a Broad Range of Semantic Relations.

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Distributional semantic models (DSMs) capture word similarity well. Specific models like Skip-gram and CBOW excel at similarity, while GloVe better identifies thematic roles and event relations.

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Psycholinguistics

Background:

  • Distributional semantic models (DSMs) extract semantic information from text.
  • Existing research often uses limited data focusing on semantic similarity.
  • The scope of semantic relations captured by DSMs remains an open question.

Purpose of the Study:

  • To evaluate the ability of eight popular DSMs to detect a wide range of semantic relations.
  • To compare model performance across different types of semantic relationships and word properties.

Main Methods:

  • Tested eight DSMs, including GloVe, Skip-gram, and CBOW variations.
  • Utilized a comprehensive set of 19 human-rated semantic relation sets.
  • Included words from various syntactic classes and across the abstract-concrete spectrum.

Main Results:

  • DSMs generally perform best at capturing semantic similarity.
  • Verb-noun thematic role relations and noun-noun event-based relations were also captured.
  • Skip-gram and CBOW excelled in similarity, while GloVe led in thematic role and event-based relations.

Conclusions:

  • DSMs demonstrate varying capabilities in capturing different semantic relations.
  • Model selection should consider the specific semantic relations of interest.
  • Findings have implications for using DSMs in cognitive and psycholinguistic research.