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In research data management, descriptive metadata are indispensable to describing data and are a key element in preparing data according to the FAIR principles (Wilkinson et al., 2016). Extracting semantic metadata from textual research data is currently not part of most metadata workflows, even more so if a research data set can be subdivided into smaller parts, such as a newspaper corpus containing multiple newspaper articles. Our approach is to add semantic metadata at the text level to facilitate the search over data. We show how to enrich metadata with three NLP methods: named entity recognition, keyword extraction, and topic modeling. The goal is to make it possible to search for texts that are about certain topics or described by certain keywords, or to identify people, places, and organisations mentioned in texts without actually having to read them and at the same time facilitate the creation of task-tailored subcorpora. To enhance this usability of the data we explore options based on the German Reference Corpus DeReKo, the largest linguistically motivated collection of German language material (Kupietz & Keibel, 2009; Kupietz et al., 2010, 2018), which contains multiple newspapers, books, transcriptions, etc., and enrich its metadata on the level of subportions, i.e. newspaper articles. We received access to a number of data files in DeReKo’s native XML format, I5. To develop the methodology, we focus on a single XML file containing all issues of one newspaper of a whole year. The following sections only give an overview of our approach, we intend, however, to provide a detailed description of the experiments and the selection of data in a subsequent longer contribution.
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