OldBERTur: Named Entity Recognition for Medieval Icelandic

Abstract

We present OldBERTur, a Named Entity Recognition (NER) model for Old Icelandic available in two variations, one for normalised texts, and one for diplomatic texts. Using a BERT-based model architecture, we fine-tune an existing BERT language model, and due to training data scarcity, we employ multiple training configurations, including pre-training domain adaptation, sentence-level data resampling, and modern Icelandic data augmentation; achieving a 93 F1 score for normalised texts, and 79 for diplomatic texts. We find that additional training configurations, such as resampling entity-annotated Old Icelandic texts, significantly improve performance in low-resource settings, while the effectiveness of added training configurations diminishes as the available training data increases. Our models can be used to automatically identify and classify person and location names in texts sourced from the rich Icelandic medieval literary tradition. Our models, along with their data and code, are made publicly available to allow for reuse and future research into medieval Scandinavian NLP and beyond.

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