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

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.