It is well-known that many words ’ meanings vary depending on the context in which they are used. This phenomenon has proven difficult to model, and recent work (Klepousniotou, Titone, & Romero, 2008; Erk, McCarthy, & Gaylord, 2009; Brown, 2010) indicates that meaning-in-context varies in a manner much more subtle than would be represented by a dictionary, including reliable fine-grained intuitions about the similarity in meaning of two occurrences of a word in different contexts. This raises the question of how to predict these intuitions of semantic similarity on the basis of contextual information. I present the results of a Magnitude Estimation task in which participants judged the similarity in meaning between pairs of verb occurrences. Stimuli consisted of pairs of sentences containing a present tense intransitive motion verb and a singular definite NP subject (e.g. “The kid runs ” vs. “The rabbit runs”). As the only varying component of sentential context, differences between the subject nouns in these pairs are hypothesized to predict intuitions about verbal semantic similarity. I explore four measures of noun similarity – two based on noun animacy, as well as conceptual and distributional similarity measures. I find that individually, all of these measures are significant predictors of verbal semantic similarity judgments, but that the best model of participant judgments combines all four. This is taken as an indication that a proper use of converging sources of evidence enables more accurate, detailed study of the perception of word meaning in context
