In this paper we look at a task at border of natural language processing, historical linguistics and the study of language development, namely that of identifying the time when a text was written. We use machine learning classification using lexical, word ending and dictionary-based features, with linear support vector machines and random forests. We find that lexical features are the most help...

We describe and evaluate a prototype system for recognising person and place names in digitised records of British parliamentary proceedings from the late 17th and early 19th centuries. The output of an OCR engine is the input for our system and we describe certain issues and errors in this data and discuss the methods we have used to overcome the problems. We describe our rule-based named enti...

Even though historical texts reveal a lot of interesting information on culture and social structure in the past, information access is limited and in most cases the only way to find the information you are looking for is to manually go through large volumes of text, searching for interesting text segments. In this paper we will explore the idea of facilitating this timeconsuming manual effort,...

As more historical texts are digitized, there is interest in applying natural language processing tools to these archives. However, the performance of these tools is often unsatisfactory, due to language change and genre differences. Spelling normalization heuristics are the dominant solution for dealing with historical texts, but this approach fails to account for changes in usage and vocabula...

This paper deals with normalization of language data from Early New High German. We describe an unsupervised, rulebased approach which maps historical wordforms to modern wordforms. Rules are specified in the form of context-aware rewrite rules that apply to sequences of characters. They are derived from two aligned versions of the Luther bible and weighted according to their frequency. The eva...