Multiword Expressions: A Pain in the Neck for NLP
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
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
Automatic identification of non-compositional phrases
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Extracting the unextractable: a case study on verb-particles
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
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
Unsupervised type and token identification of idiomatic expressions
Computational Linguistics
Unsupervised Classification of Verb Noun Multi-Word Expression Tokens
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
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
A measure of syntactic flexibility for automatically identifying multiword expressions in corpora
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Distinguishing subtypes of multiword expressions using linguistically-motivated statistical measures
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
An extensive empirical study of collocation extraction methods
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Verb noun construction MWE token supervised classification
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
A re-examination of lexical association measures
MWE '09 Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications
Handling sparsity for verb noun MWE token classification
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Measuring the non-compositionality of multiword expressions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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One of the main tasks related to multiword expressions (MWEs) is compound verb identification. There have been so many works on unsupervised identification of multiword verbs in many languages, but there has not been any conspicuous work on Persian language yet. Persian multiword verbs (known as compound verbs), are a kind of light verb construction (LVC) that have syntactic flexibility such as unrestricted word distance between the light verb and the nonverbal element. Furthermore, the nonverbal element can be inflected. These characteristics have made the task in Persian very difficult. In this paper, two different unsupervised methods have been proposed to automatically detect compound verbs in Persian. In the first method, extending the concept of pointwise mutual information (PMI) measure, a bootstrapping method has been applied. In the second approach, K-means clustering algorithm is used. Our experiments show that the proposed approaches have gained results superior to the baseline which uses PMI measure as its association metric.