We present the first annotated resource for the aspectual classification of German verb tokens in their clausal context. We use aspectual features compatible with the plurality of aspectual classifications in previous work and treat aspectual ambiguity systematically. We evaluate our corpus by using it to train supervised classifiers to automatically assign aspectual categories to verbs in context, permitting favourable comparisons to previous work.

We describe a state-of-the-art automatic system that can acquire subcategorisation frames from raw text for a free word-order language.We use it to construct a subcategorisation lexicon of German verbs from a large Web page corpus.With an automatic verb classification paradigm we evaluate our subcategorisation lexicon against a previous classification of German verbs; the lexicon produced by our system performs better than the best previous results.

We present a comparison of different selectional preference models and evaluate them on an automatic verb classification task in German.We find that all the models we compare are effective for verb clustering; the best-performing model uses syntactic information to induce nouns classes from unlabelled data in an unsupervised manner.A very simple model based on lexical preferences is also found to perform well.