Foundations of statistical natural language processing
Foundations of statistical natural language processing
Retrieving collocations from text: Xtract
Computational Linguistics - Special issue on using large corpora: I
Methods for the qualitative evaluation of lexical association measures
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Extracting the unextractable: a case study on verb-particles
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Combining association measures for collocation extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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
Deep lexical acquisition of verb-particle constructions
Computer Speech and Language
A rapid method to extract multiword expressions with statistic measures and linguistic rules
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Improving bilingual projections via sparse covariance matrices
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Unsupervised identification of persian compound verbs
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Information Sciences: an International Journal
Statistical metaphor processing
Computational Linguistics
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We review lexical Association Measures (AMs) that have been employed by past work in extracting multiword expressions. Our work contributes to the understanding of these AMs by categorizing them into two groups and suggesting the use of rank equivalence to group AMs with the same ranking performance. We also examine how existing AMs can be adapted to better rank English verb particle constructions and light verb constructions. Specifically, we suggest normalizing (Pointwise) Mutual Information and using marginal frequencies to construct penalization terms. We empirically validate the effectiveness of these modified AMs in detection tasks in English, performed on the Penn Treebank, which shows significant improvement over the original AMs.