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Grammis is a web-based information system on German grammar, hosted by the Institute for the German Language (IDS). It is human-oriented and features different theoretical perspectives on grammar. Currently, the terminology component of grammis is being redesigned for this theoretical diversity to play a more prominent role in the data model. This also opens opportunities for implementing some machine-oriented features. In this paper, we present the re-design of both data model and knowledge base. We explore how the addition of machine-oriented features to the data model impacts the knowledge base; in particular, how this addition shifts some of the textual complexity into the data model. We show that our resource can easily be ported to a SKOS-XL representation, which makes it available for data science, knowledge-based NLP applications, and LOD in the context of digital humanities.
In this paper, a method for measuring synchronic corpus (dis-)similarity put forward by Kilgarriff (2001) is adapted and extended to identify trends and correlated changes in diachronic text data, using the Corpus of Historical American English (Davies 2010a) and the Google Ngram Corpora (Michel et al. 2010a). This paper shows that this fully data-driven method, which extracts word types that have undergone the most pronounced change in frequency in a given period of time, is computationally very cheap and that it allows interpretations of diachronic trends that are both intuitively plausible and motivated from the perspective of information theory. Furthermore, it demonstrates that the method is able to identify correlated linguistic changes and diachronic shifts that can be linked to historical events. Finally, it can help to improve diachronic POS tagging and complement existing NLP approaches. This indicates that the approach can facilitate an improved understanding of diachronic processes in language change.
Frimer et al. (2015) claim that there is a linear relationship between the level of prosocial language and the level of public disapproval of US Congress. A re-analysis demonstrates that this relationship is the result of a misspecified model that does not account for first-order autocorrelated disturbances. A Stata script to reproduce all presented results is available as an appendix.