Transform historical texts into analyzable data using NLP tools, machine learning algorithms, automated parsing systems, and AI-powered analysis of linguistic evolution.
Advanced techniques for creating, annotating, and analyzing diachronic corpora. Learn to navigate the complex challenges of integrating digital heritage databases with linguistic research.
Contemporary theoretical frameworks in diachronic linguistics, from models enhanced by big data to AI-assisted reconstruction of linguistic prehistory.
Understanding how grammatical items and categories develop in natural language, covering phenomena from central grammatical areas such as tense, aspect, mood, modality, negation, and sentence structure.
Corpus-driven approaches to identifying contact-induced change, enriching annotated corpora with new tools and methods. Analysis of language contact and borrowing through translations.
Computational tools and statistical models to reconstruct linguistic family trees and trace the historical pathways of language change.