7 accepted papers
Analysing texts spanning long periods of time is critical for researchers in historical linguistics and related disciplines. However, publicly available corpora suitable for such analyses are scarce. The Project Gutenberg (PG) corpus presents a significant yet underutilized opportunity in this conte…
The annotation and exploration of large text corpora, both automatic and manual, presents significant challenges across multiple disciplines, including linguistics, digital humanities, biology, and legal science. These challenges are exacerbated by the heterogeneity of processing methods, which comp…
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, r…
In 2022, the largest German-speaking corpus of parliamentary protocols from three different centuries, on a national and federal level from the countries of Germany, Austria, Switzerland and Liechtenstein, was collected and published - GerParCor. Through GerParCor, it became possible to provide for…
A useful semantic role-annotated resource for training semantic role models for the German language is missing. We point out some problems of previous resources and provide a new one due to a combined translation and alignment process: The gold standard CoNLL-2012 semantic role annotations are trans…
Various historical languages, which used to be lingua franca of science and arts, deserve the attention of current NLP research. In this work, we take the first data-driven steps towards this research line for Classical Arabic (CA) by addressing named entity recognition (NER) and topic modeling (TM)…
Transformer-based models are now predominant in NLP.They outperform approaches based on static models in many respects. This success has in turn prompted research that reveals a number of biases in the language models generated by transformers. In this paper we utilize this research on biases to inv…