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Báo cáo khoa học: "Combining EM Training and the MDL Principle for an Automatic Verb Classification incorporating Selectional Preferences"

This paper presents an innovative, complex approach to semantic verb classification that relies on selectional preferences as verb properties. The probabilistic verb class model underlying the semantic classes is trained by a combination of the EM algorithm and the MDL principle, providing soft clusters with two dimensions (verb senses and subcategorisation frames with selectional preferences) as a result. A language-model-based evaluation shows that after 10 training iterations the verb class model results are above the baseline results. . | Combining EM Training and the MDL Principle for an Automatic Verb Classification incorporating Selectional Preferences Sabine Schulte im Walde Christian Hying Christian Scheible Helmut Schmid Institute for Natural Language Processing University of Stuttgart Germany schulte hyingcn scheibcn schmid @ims.uni-stuttgart.de Abstract This paper presents an innovative complex approach to semantic verb classification that relies on selectional preferences as verb properties. The probabilistic verb class model underlying the semantic classes is trained by a combination of the EM algorithm and the MDL principle providing soft clusters with two dimensions verb senses and subcategorisation frames with selectional preferences as a result. A language-model-based evaluation shows that after 10 training iterations the verb class model results are above the baseline results. 1 Introduction In recent years the computational linguistics community has developed an impressive number of semantic verb classifications i.e. classifications that generalise over verbs according to their semantic properties. Intuitive examples of such classifications are the Motion with a Vehicle class including verbs such as drive fly row etc. or the Break a Solid Surface with an Instrument class including verbs such as break crush fracture smash etc. Semantic verb classifications are of great interest to computational linguistics specifically regarding the pervasive problem of data sparseness in the processing of natural language. Up to now such classifications have been used in applications such as word sense disambiguation Dorr and Jones 1996 Kohomban and Lee 2005 machine translation Prescher et al. 2000 Koehn and Hoang 2007 document classification Klavans and Kan 1998 and in statistical lexical acquisition in general Rooth et al. 1999 Merlo and Stevenson 2001 Korhonen 2002 Schulte im Walde 2006 . Given that the creation of semantic verb classifications is not an end task in itself but depends on the .