Making large-scale support vector machine learning practical
Advances in kernel methods
Machine Learning
Multiword Expressions: A Pain in the Neck for NLP
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
TINLAP '75 Proceedings of the 1975 workshop on Theoretical issues in natural language processing
Idiomatic object usage and support verbs
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Automatic identification of non-compositional phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Methods for the qualitative evaluation of lexical association measures
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A statistical approach to the semantics of verb-particles
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Detecting a continuum of compositionality in phrasal verbs
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
An empirical model of multiword expression decomposability
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Mining complex predicates in Hindi using a parallel Hindi-English corpus
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
Stepwise mining of multi-word expressions in Hindi
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
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Recognition of Multi-word Expressions (MWEs) and their relative compositionality are crucial to Natural Language Processing. Various statistical techniques have been proposed to recognize MWEs. In this paper, we integrate all the existing statistical features and investigate a range of classifiers for their suitability for recognizing the non-compositional Verb-Noun (V-N) collocations. In the task of ranking the V-N collocations based on their relative compositionality, we show that the correlation between the ranks computed by the classifier and human ranking is significantly better than the correlation between ranking of individual features and human ranking. We also show that the properties ‘Distributed frequency of object' (as defined in [27] ) and ‘Nearest Mutual Information' (as adapted from [18]) contribute greatly to the recognition of the non-compositional MWEs of the V-N type and to the ranking of the V-N collocations based on their relative compositionality.