Fusing distributional and experiential information for measuring semantic relatedness

Yair Neuman, Dan Assaf, Yohai Cohen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Models of semantic relatedness have usually focused on language-based distributional information without taking into account "experiential data" concerning the embodied sensorial source of the represented concepts. In this paper, we present an integrative cognitive model of semantic relatedness. The model - semantic family resemblance - uses a variation of the co-product as a mathematical structure that guides the fusion of distributional and experiential information. Our algorithm provides superior results in a set expansion task and a significant correlation with two benchmarks of human rated word-pair similarity datasets.

Original languageEnglish
Pages (from-to)281-287
Number of pages7
JournalInformation Fusion
Volume14
Issue number3
DOIs
StatePublished - 1 Jan 2013

Keywords

  • Cognition
  • Family resemblance
  • Interdisciplinary research
  • Semantic relatedness
  • Semantic representation
  • Semiotics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

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