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Automatic induction of semantic verb classes is one of the most challenging tasks in computational lexical semantics with a wide variety of applications in natural language processing. The large number of Persian speakers and the lack of such semantic classes for Persian verbs have motivated us to use unsupervised algorithms for Persian verb clustering. In this paper, we have done experiments on inducing the semantic classes of Persian verbs based on Levin's theory for verb classes. Syntactic information extracted from dependency trees is used as base features for clustering the verbs. Since there has been no manual classification of Persian verbs prior to this paper, we have prepared a... 

Recently, there has been renewed interest in semantic word representation also called word embedding, in a wide variety of natural language processing tasks requiring sophisticated semantic and syntactic information. The quality of word embedding methods is usually evaluated based on English language benchmarks. Nevertheless, only a few studies analyze word embedding for low resource languages such as Persian. In this paper, we perform such an extensive word embedding evaluation in Persian language based on a set of lexical semantics tasks named analogy, concept categorization, and word semantic relatedness. For these evaluation tasks, we provide three benchmark data sets to show the...