From a practical point of view, a verb classification supports Natural Language Processing tasks, since it provides a principled basis for filling gaps in available lexical knowledge. For example, the English verb classification has been used for applications such as machine translation (Dorr, 1997), word sense disambiguation (Dorr and Jones, 1996), and document classification (Klavans and Kan, 1998).
In the talk, I will describe the application of k-Means, a standard clustering technique, to the task of inducing semantic classes for German verbs. A robust statistical parser was used to obtain lexical verb information, referring to purely syntactic frame definitions as well as selectional preferences. The goal of a series of cluster analyses based on the subcategorisation information was (i) to find good values for the linguistically uninteresting parameters of the clustering process, and (ii) to explore the role of the frame descriptions in verb classification. The automatic clustering was evaluated against independently motivated, hand-constructed semantic verb classes.