Spectral clustering for German verbs
C. Brew, Sabine Schulte im Walde · 2002
We describe and evaluate the application of a spectral clustering technique (Ng et al., 2002) to the unsupervised clustering of German verbs. Our previous work has shown that standard clustering techniques succeed in inducing Levin-style semantic classes from verb subcategorisation information. But clustering in the very high dimensional spaces that we use is fraught with technical and conceptual difficulties. Spectral clustering performs a dimensionality reduction on the verb frame patterns, and provides a robustness and efficiency that standard clustering methods do not display in direct use. The clustering results are evaluated according to the alignment (Christianini et al., 2002) between the Gram matrix defined by the cluster output and the corresponding matrix defined by a gold standard.