We introduce Gutenberg+, a temporally more faithful version of the Project Gutenberg (PG) corpus, one of the most widely used resources for diachronic text analysis. Despite its popularity, the PG corpus contains a major yet overlooked flaw: around 15% of its entries are collections (e.g., anthologies of books, letters, or poems) rather than atomic works, which distorts temporal analyses since such collections may span multiple decades. We present an automatic method to detect and split these collections into their constituent works, producing a finer-grained and temporally consistent corpus. We further re-annotate publication years using LLM-based retrieval-augmented generative methods, demonstrating the potential of LLMs to enhance structured linguistic resources. To illustrate the utility of Gutenberg+, we conduct a small-scale diachronic case study on negation, showing that our refined corpus captures more nuanced cross-linguistic variation than the original PG data. Finally, we release the corpus in UIMA format with full metadata and linguistic annotations, providing a standardized resource for future research on diachronic language change.

Chain-of-thought (CoT) reasoning is widely used in large language models (LLMs), but the resulting reasoning traces remain underexplored.We study these traces through the lens of discourse-level negation.Specifically, we distinguish between corrective negation, which rejects a prior reasoning step, and refining negation, which narrows or qualifies it, and introduce metrics to quantify their use in human- and LLM-authored reasoning traces.Across multiple benchmarks, we find that negation occurs much more frequently in intermediate reasoning traces than in final response texts.We then test whether negation-based features provide predictive and descriptive signal for correctness, model identity, and human-vs.-LLM authorship.For correctness prediction, negation-based features consistently outperform simple structural baselines and in several settings add complementary signal to embedding-based representations, although embeddings remain stronger overall.In a controlled comparison on correct human and LLM traces from the same dataset, our strongest results arise in human-vs.-LLM classification, where negation features outperform both structural and embedding baselines.Overall, these findings position discourse-level negation as an interpretable feature for reasoning-trace analysis, with especially strong utility for provenance-related classification and modest but consistent value for correctness prediction.

Despite the communicative importance of negation, its detection remains challenging. Previous approaches perform poorly in out-of-domain scenarios, and progress outside of English has been slow due to a lack of resources and robust models. To address this gap, we present D-Neg: a syntax-aware graph reasoning model based on a transformer that incorporates syntactic embeddings by attention-gating. D-Neg uses graph attention to represent syntactic structures, emulating the effectiveness of rule-based dependency approaches for negation detection. We train D-Neg using 7 English resources and their translations into 10 languages, all aligned at the annotation level. We conduct an evaluation of all these datasets in in-domain and out-of-domain settings. Our work represents a significant advance in negation detection, enabling more effective cross-lingual research.

Despite their success, LLMs are too computationally expensive to replace task- or domain-specific NLP systems. However, the variety of corpus formats makes reusing these systems difficult. This underscores the importance of maintaining an interoperable NLP landscape. We address this challenge by pursuing two objectives: standardizing corpus formats and enabling massively parallel corpus processing. We present a unified conversion framework embedded in a massively parallel, microservice-based, programming language-independent NLP architecture designed for modularity and extensibility. It allows for the integration of external NLP conversion tools and supports the addition of new components that meet basic compatibility requirements. To evaluate our dual data- and process-oriented approach to standardization, we (1) benchmark its efficiency in terms of processing speed and memory usage, (2) demonstrate the benefits of standardized corpus formats for NLP downstream tasks, and (3) illustrate the advantages of incorporating custom formats into a corpus format ecosystem.

Purpose: Based on the examples of English and German, we investigate to what extent parsers trained on modern variants of these languages can be transferred to older language levels without loss. Methods: We developed a treebank called DoTT (https://github.com/texttechnologylab/DoTT) which covers, roughly, the time period from 1800 until today, in conjunction with the further development of the annotation tool DependencyAnnotator. DoTT consists of a collection of diachronic corpora enriched with dependency annotations using 3 parsers, 6 pre-trained language models, 5 newly trained models for German, and two tag sets (TIGER and Universal Dependencies). To assess how the different parsers perform on texts from different time periods, we created a gold standard sample as a benchmark. Results: We found that the parsers/models perform quite well on modern texts (document-level LAS ranging from 82.89 to 88.54) and slightly worse on older texts, as expected (average document-level LAS 84.60 vs. 86.14), but not significantly. For German texts, the (German) TIGER scheme achieved slightly better results than UD. Conclusion: Overall, this result speaks for the transferability of parsers to past language levels, at least dating back until around 1800. This very transferability, it is however argued, means that studies of language change in the field of dependency syntax can draw on dependency distance but miss out on some grammatical phenomena.

Parliamentary debates represent a large and partly unexploited treasure trove of publicly accessible texts. In the German-speaking area, there is a certain deficit of uniformly accessible and annotated corpora covering all German-speaking parliaments at the national and federal level. To address this gap, we introduce the German Parliamentary Corpus (GerParCor). GerParCor is a genre-specific corpus of (predominantly historical) German-language parliamentary protocols from three centuries and four countries, including state and federal level data. In addition, GerParCor contains conversions of scanned protocols and, in particular, of protocols in Fraktur converted via an OCR process based on Tesseract. All protocols were preprocessed by means of the NLP pipeline of spaCy3 and automatically annotated with metadata regarding their session date. GerParCor is made available in the XMI format of the UIMA project. In this way, GerParCor can be used as a large corpus of historical texts in the field of political communication for various tasks in NLP.