Measuring the Relative Compositionality of Verb-Noun (V-N) Collocations by Integrating Features

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 899–906, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Measuring the relative compositionality of verb-noun (V-N) collocations by integrating features Sriram Venkatapathy � Language Technologies Research Centre, International Institute of Information Technology - Hyderabad, Hyderabad, India. sriram@research.iiit.ac.in Aravind K. Joshi Department of Computer and Information Science and Institute for Research in Cognitive Science, University of Pennsylvania, Philadelphia, PA, USA. joshi@linc.cis.upenn.edu Abstract Measuring the relative compositionality of Multi-word Expressions (MWEs) is crucial to Natural Language Processing. Various collocation based measures have been proposed to compute the relative compositionality of MWEs. In this paper, we define novel measures (both colloca- tion based and context based measures) to measure the relative compositionality of MWEs of V-N type. We show that the correlation of these features with the hu- man ranking is much superior to the cor- relation of the traditional features with the human ranking. We then integrate the pro- posed features and the traditional features using a SVM based ranking function to rank the collocations of V-N type based on their relative compositionality. We then show that the correlation between the ranks computed by the SVM based rank- ing function and human ranking is signif- icantly better than the correlation between ranking of individual features and human ranking. 1 Introduction The main goal of the work presented in this paper is to examine the relative compositionality of col- 1Part of the work was done at Institute for Research in Cog- nitive Science (IRCS), University of Pennsylvania, Philadel- phia, PA 19104, USA, when he was visiting IRCS as a Visiting Scholar, February to December, 2004. locations of V-N type using a SVM based ranking function. Measuring the relative compositionality of V-N collocations is extremely helpful in applications such as machine translation where the collocations that are highly non-compositional can be handled in a special way (Schuler and Joshi, 2004) (Hwang and Sasaki, 2005). Multi-word expressions (MWEs) are those whose structure and meaning cannot be derived from their component words, as they occur independently. Examples include conjunctions like ‘as well as’ (meaning ‘including’), idioms like ‘kick the bucket’ (meaning ‘die’), phrasal verbs like ‘find out’ (mean- ing ‘search’) and compounds like ‘village commu- nity’. A typical natural language system assumes each word to be a lexical unit, but this assumption does not hold in case of MWEs (Becker, 1975) (Fillmore, 2003). They have idiosyncratic interpre- tations which cross word boundaries and hence are a ‘pain in the neck’ (Sag et al., 2002). They account for a large portion of the language used in day-to- day interactions (Schuler and Joshi, 2004) and so, handling them becomes an important task. A large number of MWEs have a standard syn- tactic structure but are non-compositional semanti- cally. An example of such a subset is the class of non-compositional verb-noun collocations (V-N col- locations). The class of non-compositional V-N col- locations is important because they are used very frequently. These include …

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