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Unsupervised induction of persian semantic verb classes based on syntactic information
Aminian, M ; Sharif University of Technology | 2013
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- Type of Document: Article
- DOI: 10.1007/978-3-642-38634-3_13
- Publisher: 2013
- Abstract:
- 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 manual classification of 265 verbs into 43 semantic classes. We show that spectral clustering algorithm outperforms KMeans and improves on the baseline algorithm with about 17% in Fmeasure and 0.13 in Rand index
- Keywords:
- Automatic induction ; Computational lexical semantics ; Dependency trees ; Manual classification ; NAtural language processing ; Spectral clustering algorithms ; Syntactic information ; Unsupervised algorithms ; Clustering algorithms ; Information systems ; Natural language processing systems ; Semantics
- Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Warsaw ; Volume 7912 LNCS , June , 2013 , Pages 112-124 ; 03029743 (ISSN) ; 9783642386336 (ISBN)
- URL: http://link.springer.com/chapter/10.1007%2F978-3-642-38634-3_13