Information Retrieval
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 using syntactic frames and LSA
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Detection of simple plagiarism in computer science papers
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A unified adaptive co-identification framework for high-d expression data
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
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We exploit the resources in the Arabic Treebank (ATB) for the novel task of automatically creating lexical semantic verb classes for Modern Standard Arabic (MSA). Verbs are clustered 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 information about lexical heads of the constituents in the syntactic frames, as well as parameters of the clustering algorithm. The best set of parameters yields an Fβ=1 score of 0.501, compared to a random baseline with an Fβ=1 score of 0.37.