Abstract
We address the problem of classifying multiword expression tokens in running text. We focus our study on Verb-Noun Constructions (VNC) that vary in their idiomaticity depending on context. VNC tokens are classified as either idiomatic or literal. Our approach hinges upon the assumption that a literal VNC will have more in common with its component words than an idiomatic one. Commonality is measured by contextual overlap. To this end, we set out to explore different contextual variations and different similarity measures. We also identify a new data set OPAQUE that comprises only non-decomposable VNC expressions. Our approach yields state of the art performance with an overall accuracy of 77.56% on a TEST data set and 81.66% on the newly characterized data set OPAQUE.
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Diab, M.T., Krishna, M. (2009). Unsupervised Classification of Verb Noun Multi-Word Expression Tokens. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00382-0_8
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DOI: https://doi.org/10.1007/978-3-642-00382-0_8
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