Unsupervised Classification of Verb Noun Multi-Word Expression Tokens

Authors:
Mona T. Diab;Madhav Krishna
Affiliations:
Columbia University, New York NY 10115;Columbia University, New York NY 10115
Venue:
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Year:
2009

Citing 8
Cited 2

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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.