@inproceedings{provatorova-etal-2024-young,
    title = "Too Young to {NER}: Improving Entity Recognition on {D}utch Historical Documents",
    author = "Provatorova, Vera  and
      van Erp, Marieke  and
      Kanoulas, Evangelos",
    editor = "Sprugnoli, Rachele  and
      Passarotti, Marco",
    booktitle = "Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lt4hala-1.4",
    pages = "30--35",
    abstract = "Named entity recognition (NER) on historical texts is beneficial for the field of digital humanities, as it allows to easily search for the names of people, places and other entities in digitised archives. While the task of historical NER in different languages has been gaining popularity in recent years, Dutch historical NER remains an underexplored topic. Using a recently released historical dataset from the Dutch Language Institute, we train three BERT-based models and analyse the errors to identify main challenges. All three models outperform a contemporary multilingual baseline by a large margin on historical test data.",
}

Named entity recognition (NER) on historical texts is beneficial for the field of digital humanities, as it allows to easily search for the names of people, places and other entities in digitised archives. While the task of historical NER in different languages has been gaining popularity in recent years, Dutch historical NER remains an underexplored topic. Using a recently released historical dataset from the Dutch Language Institute, we train three BERT-based models and analyse the errors to identify main challenges. All three models outperform a contemporary multilingual baseline by a large margin on historical test data.
Provatorova, Vera van Erp, Marieke Kanoulas, Evangelos
Too Young to NER: Improving Entity Recognition on Dutch Historical Documents