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We describe an algorithm for automatic classification of idiomatic andliteral expressions. Our starting point is that words in a given text segment,such as a paragraph, that are highranking representatives of a common topic ofdiscussion are less likely to be a part of an idiomatic expression. Ouradditional hypothesis is that contexts in which idioms occur, typically, aremore affective and therefore, we incorporate a simple analysis of the intensityof the emotions expressed by the contexts. We investigate the bag of wordstopic representation of one to three paragraphs containing an expression thatshould be classified as idiomatic or literal (a target phrase). We extracttopics from paragraphs containing idioms and from paragraphs containingliterals using an unsupervised clustering method, Latent Dirichlet Allocation(LDA) (Blei et al., 2003). Since idiomatic expressions exhibit the property ofnon-compositionality, we assume that they usually present different semanticsthan the words used in the local topic. We treat idioms as semantic outliers,and the identification of a semantic shift as outlier detection. Thus, thistopic representation allows us to differentiate idioms from literals usinglocal semantic contexts. Our results are encouraging.

Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions