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 collocation 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 human ranking is much superior to the correlation of the traditional features with the human ranking. We then integrate the proposed 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 ranking function and human ranking is significantly better than the correlation between ranking of individual features and human ranking.
REF measured relative compositionality of collocations having verb-noun pattern using a SVM (Support Vector Machine) based ranking function.
Measuring The Relative Compositionality Of Verb-Noun (V-N) Collocations By Integrating Features
Human Language Technology Conference And Empirical Methods In Natural Language Processing
Computer Science