Compared to standard Named Entity Recognition (NER), identifying persons,
locations, and organizations in historical texts constitutes a big challenge.
To obtain machine-readable corpora, the historical text is usually scanned and
Optical Character Recognition (OCR) needs to be performed. As a result, the
historical corpora contain errors. Also, entities like location or organization
can change over time, which poses another challenge. Overall, historical texts
come with several peculiarities that differ greatly from modern texts and large
labeled corpora for training a neural tagger are hardly available for this
domain. In this work, we tackle NER for historical German, English, French,
Swedish, and Finnish by training large historical language models. We
circumvent the need for large amounts of labeled data by using unlabeled data
for pretraining a language model. We propose hmBERT, a historical multilingual
BERT-based language model, and release the model in several versions of
different sizes. Furthermore, we evaluate the capability of hmBERT by solving
downstream NER as part of this year's HIPE-2022 shared task and provide
detailed analysis and insights. For the Multilingual Classical Commentary
coarse-grained NER challenge, our tagger HISTeria outperforms the other teams'
models for two out of three languages.

本篇研究讨论了针对历史文本进行命名实体识别的挑战，并提出使用大型历史语言模型 hmBERT 预训练来解决标注数据不充足的问题，并使用 HIPE-2022 共享任务来评估模型性能。