Experiments on the Choice of Features for Learning Verb Classes

Experiments on the Choice of Features for Learning Verb Classes Sabine Schulte im Walde Institut flir Maschinelle Sprachverarbeitung Universitat Stuttgart AzenbergstraBe 12, 70174 Stuttgart, Germany schulte@ims.uni—stuttgart.de Abstract The choice of verb features is crucial for the learning of verb classes. This pa- per presents clustering experiments on 168 German verbs, which explore the relevance of features on three levels of verb description, purely syntactic frame types, prepositional phrase information and selectional preferences. In contrast to previous approaches concentrating on the sparse data problem, we present ev- idence for a linguistically defined limit on the usefulness of features which is driven by the idiosyncratic properties of the verbs and the specific attributes of the desired verb classification. 1 Introduction The verb is central to the meaning and the struc- ture of a sentence, and lexical verb information represents the core in supporting NLP-tasks such as word sense disambiguation (Dorr and Jones, 1996; Prescher et al., 2000), machine transla- tion (Don, 1997), document classification (Kla- vans and Kan, 1998), and subcategorisation acqui- sition and filtering (Korhonen, 2002). A means to generalise over and predict common properties of verbs is captured by the constitution of verb classes. Levin (1993) has established an extensive manual classification for English verbs; computa- tional approaches adopt the linguistic hypothesis that verb meaning components to a certain extent determine verb behaviour as basis for automati- cally inducing semantic verb classes from corpus- based features (Schulte im Walde, 2000; Merlo and Stevenson, 2001; Joanis, 2002). Computational approaches on verb classifica- tion which take advantage of corpus-based and knowledge-based verb information offered by available tools and resources such as statistical parsers and semantic ontologies, suffer from se- vere problems to encode and benefit from the information, especially with respect to selec- tional preferences, cf. Schulte im Walde (2000); Joanis (2002). This paper presents clustering ex- periments on German verbs which explore the relevance of features on three levels of verb de- scription, purely syntactic frame types, preposi- tional phrase information and selectional prefer- ences. The clustering results show that the choice and implementation of verb features is crucial for the induction of the verb classes. Intuitively, one might want to add and refine features ad infinitum, but we present evidence for a linguistically defined limit on the usefulness of features which is driven by the idiosyncratic properties of the verbs and the verb classification. 2 German Verb Classes A set of 168 German verbs is manually classified into 43 concise semantic verb classes. The pur- pose of the manual classification is (i) to evaluate the reliability and performance of the clustering experiments on a preliminary set of verbs, and (ii) to explore the potential and limit to apply the clus- tering method to large-scale verb data. The Ger- man classes are closely related to the English pen- dant in (Levin, 1993) and agree with the German verb classification in (Schumacher, 1986) as far as the relevant verbs appear in his semantic 'fields'. Table 1 presents the manual verb classification. The …

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