This paper presents the EHRI Annotator, a web-based tool for multilingual named entity recognition (NER) and entity linking (EL) in Holocaust-related texts. The tool was developed to support services provided by the European Holocaust Research Infrastructure (EHRI), primarily the digital scholarly editions published by EHRI (EHRI Online Editions) by streamlining the process of detecting named entities in documents and linking them to their unique identifiers in EHRI and third-party controlled vocabularies and gazetteers. The EHRI Annotator builds upon previous work on domain-specific NER, taking it a step further to support multilingual EL. The tool adopts a dual entity linking architecture that uses a different matching approach depending on the type of the named entity. It performs semantic matching for entities to be linked to EHRI vocabularies and authority sets which are modestly sized, and string-matching-based retrieval for locations to be linked to the extensive GeoNames gazetteer using a domain-specific relevance weighting. A preliminary evaluation on 264 entities from a manually annotated dataset of Holocaust testimonies yields an Accuracy@5 of 77.7% when it comes to the linking component of the tool. User testing confirms the tool’s usability but also highlights areas for improvement.

The European Holocaust Research Infrastructure (EHRI) aims to support Holocaust research by making information about dispersed Holocaust material accessible and interconnected through its services. Creating a tool capable of detecting named entities in texts such as Holocaust testimonies or archival descriptions would make it easier to link more material with relevant identifiers in domain-specific controlled vocabularies, semantically enriching it, and making it more discoverable. With this paper, we release EHRI-NER, a multilingual dataset (Czech, German, English, French, Hungarian, Dutch, Polish, Slovak, Yiddish) for Named Entity Recognition (NER) in Holocaust-related texts. EHRI-NER is built by aggregating all the annotated documents in the EHRI Online Editions and converting them to a format suitable for training NER models. We leverage this dataset to fine-tune the multilingual Transformer-based language model XLM-RoBERTa (XLM-R) to determine whether a single model can be trained to recognize entities across different document types and languages. The results of our experiments show that despite our relatively small dataset, in a multilingual experiment setup, the overall F1 score achieved by XLM-R fine-tuned on multilingual annotations is 81.5%. We argue that this score is sufficiently high to consider the next steps towards deploying this model.