InterGramm

Interactive Grammar Analysis of Historical Texts

Shortfacts

Innovation

The empirical research project examines the expansion of Middle Low German from the 13th century to the change of written language in the 16th/17th century, when Middle Low German lost its status as a written language to Early New High German.
An 'interactive' method is being developed that combines machine learning and expert feedback. In this way, a central problem of existing annotation methods for historical texts is to be solved. Existing parsing and tagging methods in computational and corpus linguistics assume static (a priori defined) grammars or grammatical categories, which does not do justice to the historical dynamics of grammar. 'Discovering' a diachronically evolving, dynamic grammar in the corpus using rule-based text analysis procedures and machine learning methods and thus reconstructing language change on the basis of evidence is a novelty. As this requires knowledge of language/grammar history as well as knowledge in the field of computational linguistics and computer science, the project is designed as an interdisciplinary project that requires close cooperation between the disciplines over the entire funding period.