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Editorial
(2016)
This chapter will present lessons learned from CLARIN-D, the German CLARIN national consortium. Members of the CLARIN-D communities and of the CLARIN-D consortium have been engaged in innovative, data-driven, and community-based research, using language resources and tools in the humanities and neigh-bouring disciplines. We will present different use cases and users’ stories that demonstrate the innovative research potential of large digital corpora and lexical resources for the study of language change and variation, for language documentation, for literary studies, and for the social sciences. We will emphasize the added value of making language resources and tools available in the CLARIN distributed research infrastructure and will discuss legal and ethical issues that need to be addressed in the use of such an infrastructure. Innovative technical solutions for accessing digital materials still under copyright and for data mining such materials will be presented. We will outline the need for close interaction with communities of interest in the areas of curriculum development, data management, and training the next generation of digital humanities scholars. The importance of community-supported standards for encoding language resources and the practice of community-based quality control for digital research data will be presented as a crucial step toward the provisioning of high quality research data. The chapter will conclude with a discussion of impor-tant directions for innovative research and for supporting infrastructure development over the next decade and beyond.
One central goal of the project ‘Zentrum für digitale Lexikographie der deutschen Sprache’ (Center for digital lexicography for the German Language, www.zdl.org) is to provide a corpus-based lexicographic component of common German multi-word expressions (MWE), including idioms, for DWDS (www.dwds.de), a general language dictionary of contemporary German. As a central challenge of this task, we have identified an adequate lexicographic representation of such common properties of MWE as variation and modification. To document the variation, we have developed a special entry-clustering model, which we call hub-node entry. This model comprises a core hub entry headed by a short nuclear form of the MWE and several node entries, which represent the most common variants in their full lexical forms.