Simulating human ratings on word concreteness

Shi Feng, Zhiqiang Cai, Scott Crossley, Danielle S. McNamara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Psychological measures of concreteness of words are generally estimated by having humans provide ratings of words on a concreteness scale. Due to the limits of this technique, concreteness ratings in current word databases (e.g., MRC) are incomplete due to the limited size of the word samples. In this study, we use available linguistic databases to formulate a computational model to simulate human ratings on word concreteness. The computational model includes Lexical Type, Latent Semantic Analysis Dimensions, Hypernymy Levels, Word Frequency and Word Length. Our results indicate that the model accounts for 64% variance of human ratings.

Original languageEnglish (US)
Title of host publicationProceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24
Pages245-250
Number of pages6
StatePublished - Sep 9 2011
Externally publishedYes
Event24th International Florida Artificial Intelligence Research Society, FLAIRS - 24 - Palm Beach, FL, United States
Duration: May 18 2011May 20 2011

Publication series

NameProceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24

Other

Other24th International Florida Artificial Intelligence Research Society, FLAIRS - 24
CountryUnited States
CityPalm Beach, FL
Period5/18/115/20/11

ASJC Scopus subject areas

  • Artificial Intelligence

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