Verb Classes and Alternations in Bangla, German, English, and Korean
Verb Classes and Alternations in Bangla, German, English, and Korean
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Clustering verbs semantically according to their alternation behaviour
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Unsupervised induction of modern standard Arabic verb classes
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Detection of simple plagiarism in computer science papers
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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We exploit the resources in the Arabic Treebank (ATB) and Arabic Gigaword (AG) to determine the best features for the novel task of automatically creating lexical semantic verb classes for Modern Standard Arabic (MSA). The verbs are classified into groups that share semantic elements of meaning as they exhibit similar syntactic behavior. The results of the clustering experiments are compared with a gold standard set of classes, which is approximated by using the noisy English translations provided in the ATB to create Levin-like classes for MSA. The quality of the clusters is found to be sensitive to the inclusion of syntactic frames, LSA vectors, morphological pattern, and subject animacy. The best set of parameters yields an Fβ=1 score of 0.456, compared to a random baseline of an Fβ=1 score of 0.205.