Classification of Medieval Documents

Abstract

The present study is a comparative exploration of different classification tasks for Swedish medieval charters (transcriptions from the “SDHK” collection) and different classifier setups. In particular, we explored the identification of the issuer, place of issue, and decade of production. The experiments used features based on lowercased words and character 3- and 4-grams. We evaluated the performance of two learning algorithms: linear discriminant analysis and decision trees. For evaluation, five-fold cross-validation was performed. We report accuracy and macro-averaged F1 score. The validation made use of six labeled subsets of SDHK combining the three tasks with Old Swedish and Latin. Issuer identification for the Latin dataset (595 charters from 12 issuers) reached the highest scores, above 0.9, for the decision tree classifier using word features. The best accuracy for Old Swedish issuer identification was 0.81. Place and decade identification produced lower performance scores for both languages. Which classifier design is the best one seems to depend on peculiarities of the dataset and the classification task. The present study does however support the idea that text classification is useful also for medieval documents characterized by extreme spelling variation.