TY - GEN
T1 - Simulating human ratings on word concreteness
AU - Feng, Shi
AU - Cai, Zhiqiang
AU - Crossley, Scott
AU - McNamara, Danielle S.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:80052399683
SN - 9781577355014
T3 - Proceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24
SP - 245
EP - 250
BT - Proceedings of the 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24
T2 - 24th International Florida Artificial Intelligence Research Society, FLAIRS - 24
Y2 - 18 May 2011 through 20 May 2011
ER -