Inducing German Semantic Verb Classes from Purely Syntactic Subcategorisation Information

Inducing German Semantic Verb Classes from Purely Syntactic Subcategorisation Information Sabine Schulte im Walde Institut für Maschinelle Sprachverarbeitung Universität Stuttgart Azenbergstraße 12, 70174 Stuttgart, Germany schulte@ims.uni-stuttgart.de Chris Brew Department of Linguistics The Ohio State University Columbus, USA, OH 43210-1298 cbrew@ling.ohio-state.edu Abstract The paper describes the application of k- Means, a standard clustering technique, to the task of inducing semantic classes for German verbs. Using probability distributions over verb subcategorisation frames, we obtained an intuitively plausi- ble clustering of 57 verbs into 14 classes. The automatic clustering was evaluated against independently motivated, hand- constructed semantic verb classes. series of post-hoc cluster analyses ex-A plored the influence of specific frames and frame groups on the coherence of the verb classes, and supported the tight connec- tion between the syntactic behaviour of the verbs and their lexical meaning com- ponents. 1 Introduction A long-standing linguistic hypothesis asserts a tight connection between the meaning components of a verb and its syntactic behaviour: To a certain ex- tent, the lexical meaning of a verb determines its be- haviour, particularly with respect to the choice of its arguments. The theoretical foundation has been es- tablished in extensive work on semantic verb classes such as (Levin, 1993) for English and (Vázquez et al., 2000) for Spanish: each verb class contains verbs which are similar in their meaning and in their syntactic properties. From a practical point of view, a verb classifi- cation supports Natural Language Processing tasks, since it provides a principled basis for filling gaps in available lexical knowledge. For example, the En- glish verb classification has been used for applica- tions such as machine translation (Dorr, 1997), word sense disambiguation (Dorr and Jones, 1996), and document classification (Klavans and Kan, 1998). Various attempts have been made to infer conve- niently observable morpho-syntactic and semantic properties for English verb classes (Dorr and Jones, 1996; Lapata, 1999; Stevenson and Merlo, 1999; Schulte im Walde, 2000; McCarthy, 2001). To our knowledge this is the first work to ob- tain German verb classes automatically. We used a robust statistical parser (Schmid, 2000) to ac- quire purely syntactic subcategorisation information for verbs. The information was provided in form of probability distributions over verb frames for each verb. There were two conditions: the first with relatively coarse syntactic verb subcategorisa- tion frames, the second a more delicate classifica- tion subdividing the verb frames of the first con- dition using prepositional phrase information (case plus preposition). In both conditions verbs were clustered using k-Means, an iterative, unsupervised, hard clustering method with well-known properties, cf. (Kaufman and Rousseeuw, 1990). The goal of a series of cluster analyses was (i) to find good values for the parameters of the clustering process, and (ii) to explore the role of the syntactic frame descrip- tions in verb classification, to demonstrate the im- plicit induction of lexical meaning components from syntactic properties, and to suggest ways in which the syntactic information might further be refined. Our long term goal is to support the development of Computational Linguistics …

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