The paper presents current work on a German corpus annotated for metaphor. Metaphors denote entities or situations that are in some sense similar to the literal referent, e.g., when “Handschrift” ‘signature’ is used in the sense of ‘distinguishing mark’ or the suppression of hopes is introduced by the verb “verschütten” ‘bury’. The corpus is part of a project on register, hence, includes material from different registers that represent register variation along a number of important dimensions, but we believe that it is of interest to research on metaphor in general. The corpus extends previous annotation initiatives in that it not only annotates the metaphoric expressions themselves but also their respective relevant contexts that trigger a metaphorical interpretation of the expressions. For the corpus, we developed extended annotation guidelines, which specifically focus not only on the identification of these metaphoric contexts but also analyse in detail specific linguistic challenges for metaphor annotation that emerge due to the grammar of German.

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.

Abstract An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.