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.

Derived Text Formats enable the distribution of copyrighted texts by systematically perturbing linguistic information to reduce reconstructability. However, the extent to which such information loss affects downstream text classification remains unclear. We investigate how controlled perturbations affect learning dynamics in transformer-based classification using two datasets and two strategies: POS-consistent replacement of 30%, 40%, and 50% of tokens, and random word-order shuffling. On Wikipedia data, POS replacement increases loss by 4-9% and reduces micro-F1 by 3-8%, depending on the replacement rate, while shuffling raises loss by 5% and lowers micro-F1 by 4%. Performance degrades monotonically with higher replacement rates, and shuffling yields results between the 30% and 40% conditions, indicating that DistilBERT relies more on lexical semantics than on word order. Experiments on specialist-domain data show the same pattern, demonstrating robustness across domains. To test cross-representation generalization, we train classifiers on both clean and perturbed texts and evaluate them on the respective alternate representation. Models trained on DTF data generalize better to clean text than vice versa, suggesting that perturbation-based training promotes more robust representations. Our findings position DTF as a promising strategy for reproducible, legally compliant, and robust NLP research.

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.