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Simple Co‐Occurrence Statistics Reproducibly Predict Association Ratings
Authors:Markus J. Hofmann  Chris Biemann  Chris Westbury  Mariam Murusidze  Markus Conrad  Arthur M. Jacobs
Affiliation:1. Department of PsychologyUniversity of Wuppertal;2. Department of Computer ScienceUniversity of Hamburg;3. Department of PsychologyUniversity of Alberta;4. Department of PsychologyFree University Berlin
Abstract:What determines human ratings of association? We planned this paper as a test for association strength (AS) that is derived from the log likelihood that two words co‐occur significantly more often together in sentences than is expected from their single word frequencies. We also investigated the moderately correlated interactions of word frequency, emotional valence, arousal, and imageability of both words (r's ≤ .3). In three studies, linear mixed effects models revealed that AS and valence reproducibly account for variance in the human ratings. To understand further correlated predictors, we conducted a hierarchical cluster analysis and examined the predictors of four clusters in competitive analyses: Only AS and word2vec skip‐gram cosine distances reproducibly accounted for variance in all three studies. The other predictors of the first cluster (number of common associates, (positive) point‐wise mutual information, and word2vec CBOW cosine) did not reproducibly explain further variance. The same was true for the second cluster (word frequency and arousal); the third cluster (emotional valence and imageability); and the fourth cluster (consisting of joint frequency only). Finally, we discuss emotional valence as an important dimension of semantic space. Our results suggest that a simple definition of syntagmatic word contiguity (AS) and a paradigmatic measure of semantic similarity (skip‐gram cosine) provide the most general performance‐independent explanation of association ratings.
Keywords:Association strength  Co‐occurrence statistics  Semantic long‐term memory  Associative read‐out model  Interactive activation model
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