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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
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TINLAP '75 Proceedings of the 1975 workshop on Theoretical issues in natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Idiomatic object usage and support verbs
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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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
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
Context-dependent SMT model using bilingual verb-noun collocation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Unsupervised type and token identification of idiomatic expressions
Computational Linguistics
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Measuring MWE compositionality using semantic annotation
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Using information about multi-word expressions for the word-alignment task
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Distinguishing subtypes of multiword expressions using linguistically-motivated statistical measures
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Semantics-based multiword expression extraction
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Two approaches for building an unsupervised dependency parser and their other applications
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Can recognising multiword expressions improve shallow parsing?
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Semantic clustering: an attempt to identify multiword expressions in Bengali
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
Learning English light verb constructions: contextual or statistical
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
ROCLING '11 Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing
SB: mmSystem - using decompositional semantics for lexical simplification
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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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.