Simple Co-Occurrence Statistics Reproducibly Predict Association Ratings

Autor/inn/en	Hofmann, Markus J.; Biemann, Chris; Westbury, Chris; Murusidze, Mariam; Conrad, Markus; Jacobs, Arthur M.
Titel	Simple Co-Occurrence Statistics Reproducibly Predict Association Ratings
Quelle	In: Cognitive Science, 42 (2018) 7, S.2287-2312 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12662
Schlagwörter	Prediction; Correlation; Word Frequency; Emotional Response; Multivariate Analysis; Imagery; Semantics; Sentences; Computational Linguistics + Suchen Sie Ihr Suchwort? Vorhersage; Korrelation; Word analysis; Frequency; Wortanalyse; Häufigkeit; Emotionales Verhalten; Multivariate Analyse; Metaphorik; Semantik; Sentence analysis; Satzanalyse; Linguistics; Computerlinguistik
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 = 0.3). 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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01