Word association norms, mutual information, and lexicography
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
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
An empirical model of multiword expression decomposability
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German (Cognitive Technologies)
Context-dependent SMT model using bilingual verb-noun collocation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Compositionality and multiword expressions: six of one, half a dozen of the other?
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Automatic identification of non-compositional multi-word expressions using latent semantic analysis
MWE '06 Proceedings of the Workshop on Multiword Expressions: Identifying and Exploiting Underlying Properties
Hi-index | 0.01 |
The measurement of relative compositionality of bigrams is crucial to identify Multi-word Expressions (MWEs) in Natural Language Processing (NLP) tasks. The article presents the experiments carried out as part of the participation in the shared task 'Distributional Semantics and Compositionality (DiSCo)' organized as part of the DiSCo workshop in ACL-HLT 2011. The experiments deal with various collocation based statistical approaches to compute the relative compositionality of three types of bigram phrases (Adjective-Noun, Verb-subject and Verb-object combinations). The experimental results in terms of both fine-grained and coarse-grained compositionality scores have been evaluated with the human annotated gold standard data. Reasonable results have been obtained in terms of average point difference and coarse precision.