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Although pre-trained named entity recognition (NER) models are highlyaccurate on modern corpora, they underperform on historical texts due todifferences in language OCR errors. In this work, we develop a new NER corpusof 3.6M sentences from late medieval charters written mainly in Czech, Latin,and German.  We show that we can start with a list of known historical figures andlocations and an unannotated corpus of historical texts, and use informationretrieval techniques to automatically bootstrap a NER-annotated corpus. Usingour corpus, we train a NER model that achieves entity-level Precision of72.81-93.98% with 58.14-81.77% Recall on a manually-annotated test dataset.Furthermore, we show that using a weighted loss function helps to combat classimbalance in token classification tasks. To make it easy for others toreproduce and build upon our work, we publicly release our corpus, models, andexperimental code.

People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts