Automatic dating of medieval charters from Denmark

Dating of medieval text sources is a central task common to the field of manuscript studies. It is a difficult process requiring expert philological and historical knowledge. We investigate the issue of automatic dating of a collection of about 300 charters from medieval Denmark, in particular how n-gram models based on different transcription levels of the charters can be used to assign the manuscripts to a specific temporal interval. We frame the problem as a classification task by dividing the period into bins of 50 years and using these as classes in a supervised learning setting to develop SVM classifiers. We show that the more detailed facsimile transcription, which captures palaeographic characteristics of a text, provides better results than the diplomatic level, where such distinctions are normalised. Furthermore, both character and word n-grams show promising results, the highest accuracy reaching 74.96 %. This level of classification accuracy corresponds to being able to date almost 75 % of the charters with a 25-year error margin, which philologists use as a standard of the precision with which medieval texts can be dated manually.peer-reviewe

Letters from the past : modeling historical sound change through diachronic character embeddings

While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. In this paper, we address the detection of sound change through historical spelling. We propose that a sound change can be captured by comparing the relative distance through time between the distributions of the characters involved before and after the change has taken place. We model these distributions using PPMI character embeddings. We verify this hypothesis in synthetic data and then test the method’s ability to trace the well-known historical change of lenition of plosives in Danish historical sources. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distributionpeer-reviewe

Identifying temporal trends based on perplexity and clustering : Are we looking at language change?

In this work we propose a data-driven methodology for identifying temporal trends in a corpus of medieval charters. We have used perplexities derived from RNNs as a distance measure between documents and then, performed clustering on those distances. We argue that perplexities calculated by such language models are representative of temporal trends. The clusters produced using the KMeans algorithm give an insight of the differences in language in different time periods at least partly due to language change. We suggest that the temporal distribution of the individual clusters might provide a more nuanced picture of temporal trends compared to discrete bins, thus providing better results when used in a classification task.peer-reviewe

Survey and reproduction of computational approaches to dating of historical texts

Finding the year of writing for a historical text is of crucial importance to historical research. However, the year of original creation is rarely explicitly stated and must be inferred from the text content, historical records, and codicological clues. Given a transcribed text, machine learning has successfully been used to estimate the year of production. In this paper, we present an overview of several estimation approaches for historical text archives spanning from the 12th century until today

Survey and reproduction of computational approaches to dating of historical texts

Finding the year of writing for a historical text is of crucial importance to historical research. However, the year of original creation is rarely explicitly stated and must be inferred from the text content, historical records, and codicological clues. Given a transcribed text, machine learning has successfully been used to estimate the year of production. In this paper, we present an overview of several estimation approaches for historical text archives spanning from the 12th century until today