Text+ is the German distributed research data infrastructure for literary studies, linguistics, and spoken and written language. Its resources consist of contemporary and historical literary and media texts, deeply annotated material, transcripts of spoken and sign language, and original recordings. Text+ provides access to its resources according to the FAIR guidelines: Findable due to standard-conformant metadata, Accessible with single sign-on authentication, Interoperable via open data formats, and Reproducible through web services and extensive documentation. The 30+ partners of Text+ are archives, libraries, universities, and other research institutions. The partners are autonomous, and they differ in the amount of data and processing capabilities they provide. In this paper, we describe the hub architecture of Text+, which gives users a central and FAIR point of access to research data that continues to be distributed across the Text+ partner institutions. The architecture serves as a blueprint to evolving research infrastructures that aim at maintaining (and empowering) their research data contributors.

The combination of topic modeling and automatic topic labeling sheds light on understanding large corpora of text. It can be used to add semantic information for existing metadata. In addition, one can use the documents and the corresponding topic labels for topic classification. While there are existing algorithms for topic modeling readily accessible for processing texts, there is a need to postprocess the result to make the topics more interpretable and self-explanatory. The topic words from the topic model are ranked and the first/top word could easily be considered as a label. However, it is imperative to use automatic topic labeling, because the highest scored word is not the word that sums up the topic in the best way. Using the lexical-semantic word net GermaNet, the first step is to disambiguate words that are represented in GermaNet with more than one sense. We show how to find the correct sense in the context of a topic with the method of word sense disambiguation. To enhance accuracy, we present a similarity measure based on vectors of topic words that considers semantic relations of the senses demonstrating superior performance of the investigated cases compared to existing methods.