Annotated corpora -- texts marked up with information about grammatical structures -- are transforming how researchers study language change over time. However, the high cost of manual annotation has limited the size of these corpora and the research questions that can be asked. This project addresses this problem by building on and extending recent major advances in natural language processing (NLP) (a branch of artificial intelligence (AI)) to efficiently create very large collections (hundreds of millions of words spanning several centuries) of grammatically-analyzed text for two languages with rich written traditions. The availability of these new collections at scales previously not possible will enable linguists to make new discoveries about how languages change over time. It will also benefit researchers in other fields such as history, literature, and heritage and cultural studies, which in many cases currently rely on simple searches for individual words or sequences of words. Moreover, the AI techniques developed to carry out this work can also be applied to other languages with large amounts of unannotated text, with similar benefits to researchers in linguistics, history, and literature. The manually annotated resources developed for the project will be of great interest to NLP researchers. This project addresses a critical limitation in historical linguistics research: while many hypotheses about language change require tens or hundreds of millions of words