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Media type:
E-Article
Title:
Experiments on the Automatic Induction of German Semantic Verb Classes
Contributor:
im Walde, Sabine Schulte
Published:
MIT Press - Journals, 2006
Published in:
Computational Linguistics, 32 (2006) 2, Seite 159-194
Language:
English
DOI:
10.1162/coli.2006.32.2.159
ISSN:
0891-2017;
1530-9312
Origination:
Footnote:
Description:
This article presents clustering experiments on German verbs: A statistical grammar model for German serves as the source for a distributional verb description at the lexical syntax-semantics interface, and the unsupervised clustering algorithm k-means uses the empirical verb properties to perform an automatic induction of verb classes. Various evaluation measures are applied to compare the clustering results to gold standard German semantic verb classes under different criteria. The primary goals of the experiments are (1) to empirically utilize and investigate the well-established relationship between verb meaning and verb behavior within a cluster analysis and (2) to investigate the required technical parameters of a cluster analysis with respect to this specific linguistic task. The clustering methodology is developed on a small-scale verb set and then applied to a larger-scale verb set including 883 German verbs.