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

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2019–2027, October 25-29, 2014, Doha, Qatar. c⃝2014 Association for Computational Linguistics Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions Jing Peng & Anna Feldman Computer Science/Linguistics Montclair State University Montclair, New Jersey, USA {pengj,feldmana}@mail.montclair.edu Ekaterina Vylomova Computer Science Bauman State Technical University Moscow, Russia evylomova@gmail.com Abstract We describe an algorithm for automatic clas- sification of idiomatic and literal expressions. Our starting point is that words in a given text segment, such as a paragraph, that are high- ranking representatives of a common topic of discussion are less likely to be a part of an id- iomatic expression. Our additional hypothesis is that contexts in which idioms occur, typi- cally, are more affective and therefore, we in- corporate a simple analysis of the intensity of the emotions expressed by the contexts. We investigate the bag of words topic represen- tation of one to three paragraphs containing an expression that should be classified as id- iomatic or literal (a target phrase). We ex- tract topics from paragraphs containing idioms and from paragraphs containing literals us- ing an unsupervised clustering method, Latent Dirichlet Allocation (LDA) (Blei et al., 2003). Since idiomatic expressions exhibit the prop- erty of non-compositionality, we assume that they usually present different semantics than the words used in the local topic. We treat idioms as semantic outliers, and the identifi- cation of a semantic shift as outlier detection. Thus, this topic representation allows us to dif- ferentiate idioms from literals using local se- mantic contexts. Our results are encouraging. 1 Introduction The definition of what is literal and figurative is still object of debate. Ariel (2002) demonstrates that lit- eral and non-literal meanings cannot always be distin- guished from each other. Literal meaning is originally assumed to be conventional, compositional, relatively context independent, and truth conditional. The prob- lem is that the boundary is not clear-cut, some figu- rative expressions are compositional – metaphors and many idioms; others are conventional – most of the id- ioms. Idioms present great challenges for many Natu- ral Language Processing (NLP) applications. They can violate selection restrictions (Sporleder and Li, 2009) as in push one’s luck under the assumption that only concrete things can normally be pushed. Idioms can disobey typical subcategorization constraints (e.g., in line without a determiner before line), or change the default assignments of semantic roles to syntactic cate- gories (e.g., in X breaks something with Y, Y typically is an instrument but for the idiom break the ice, it is more likely to fill a patient role as in How to break the ice with a stranger). In addition, many potentially id- iomatic expressions can be used either literally or fig- uratively, depending on the context. This presents a great challenge for machine translation. For example, a machine translation system must translate held fire differently in Now, now, hold your fire until I’ve had a chance to explain. Hold your fire, Bill. …

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