Combining EM Training and the MDL Principle for an Automatic Verb Classification Incorporating Selectional Preferences

Proceedings of ACL-08: HLT, pages 496–504, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics 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 prop- erties. The probabilistic verb class model un- derlying 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 subcategori- sation 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 re- sults. 1 Introduction In recent years, the computational linguistics com- munity has developed an impressive number of se- mantic verb classifications, i.e., classifications that generalise over verbs according to their semantic properties. Intuitive examples of such classifica- tions are the MOTION WITH A VEHICLE class, in- cluding verbs such as drive, fly, row, etc., or the BREAK A SOLID SURFACE WITH AN INSTRUMENT class, including verbs such as break, crush, frac- ture, smash, etc. Semantic verb classifications are of great interest to computational linguistics, specifi- cally regarding the pervasive problem of data sparse- ness in the processing of natural language. Up to now, such classifications have been used in applica- tions 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 gen- eral (Rooth et al., 1999; Merlo and Stevenson, 2001; Korhonen, 2002; Schulte im Walde, 2006). Given that the creation of semantic verb classi- fications is not an end task in itself, but depends on the application scenario of the classification, we find various approaches to an automatic induction of semantic verb classifications. For example, Siegel and McKeown (2000) used several machine learn- ing algorithms to perform an automatic aspectual classification of English verbs into event and sta- tive verbs. Merlo and Stevenson (2001) presented an automatic classification of three types of English intransitive verbs, based on argument structure and heuristics to thematic relations. Pereira et al. (1993) and Rooth et al. (1999) relied on the Expectation- Maximisation algorithm to induce soft clusters of verbs, based on the verbs’ direct object nouns. Sim- ilarly, Korhonen et al. (2003) relied on the Informa- tion Bottleneck (Tishby et al., 1999) and subcate- gorisation frame types to induce soft verb clusters. This paper presents an innovative, complex ap- proach to semantic verb classes that relies on se- lectional preferences as verb properties. The un- derlying linguistic assumption for this verb class model is that verbs which agree on their selec- tional preferences belong to a common seman- tic class. The model is implemented as a soft- clustering approach, in order to capture the poly- semy …

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