This paper describes an application system aimed to help lexicographers in the extraction of example sentences for a given headword based on its different senses. The tool uses classification and clustering methods and incorporates user feedback to refine its results.

Currently, research infrastructures are being designed and established in many disciplines since they all suffer from an enormous fragmentation of their resources and tools. In the domain of language resources and tools the CLARIN initiative has been funded since 2008 to overcome many of the integration and interoperability hurdles. CLARIN can build on knowledge and work from many projects that were carried out during the last years and wants to build stable and robust services that can be used by researchers. Here service centres will play an important role that have the potential of being persistent and that adhere to criteria as they have been established by CLARIN. In the last year of the so-called preparatory phase these centres are currently developing four use cases that can demonstrate how the various pillars CLARIN has been working on can be integrated. All four use cases fulfil the criteria of being cross-national.

We report about a project which brings together Natural Language Processing and eLearning. One of the functionalities developed within this project is the possibility to annotate learning objects semi-automatically with keywords. To this end, a keyword extractor has been created which is able to handle documents in 8 languages. The approach employed is based on a linguistic processing step which is followed by a filtering step of candidate keywords and their subsequent ranking based on frequency criteria. Three tests have been carried out to provide a rough evaluation of the performance of the tool, to measure inter annotator agreement in order to determine the complexity of the task and to evaluate the acceptance of the proposed keywords by users.

In this paper we will focus on the lexical-semantic relations in the German wordnet GermaNet. It has been shown that wordnets suffer from the relatively small number of relations between their lexical objects. It is assumed that applications in NLP and IR, in particular those relying on word sense disambiguation, can be boosted by a higher relational density of the lexical resource. We report on research and experiments in the lexical acquisition of a new type of relation from a large annotated German newspaper corpus, i.e. the relation between the verbal head of a predicate and the nominal head of its argument. We investigate how the insertion of instances of this relation into the German wordnet GermaNet affects the overall structure of the wordnet as well as the neighbourhood of the nodes which are connected by an instance of the new relation.