  This work introduces HistNERo, the first Romanian corpus for Named Entity
Recognition (NER) in historical newspapers. The dataset contains 323k tokens of
text, covering more than half of the 19th century (i.e., 1817) until the late
part of the 20th century (i.e., 1990). Eight native Romanian speakers annotated
the dataset with five named entities. The samples belong to one of the
following four historical regions of Romania, namely Bessarabia, Moldavia,
Transylvania, and Wallachia. We employed this proposed dataset to perform
several experiments for NER using Romanian pre-trained language models. Our
results show that the best model achieved a strict F1-score of 55.69%. Also, by
reducing the discrepancies between regions through a novel domain adaption
technique, we improved the performance on this corpus to a strict F1-score of
66.80%, representing an absolute gain of more than 10%.
