  Named Entity Recognition (NER), search, classification and tagging of names
and name like frequent informational elements in texts, has become a standard
information extraction procedure for textual data. NER has been applied to many
types of texts and different types of entities: newspapers, fiction, historical
records, persons, locations, chemical compounds, protein families, animals etc.
In general a NER system's performance is genre and domain dependent and also
used entity categories vary (Nadeau and Sekine, 2007). The most general set of
named entities is usually some version of three partite categorization of
locations, persons and organizations. In this paper we report first large scale
trials and evaluation of NER with data out of a digitized Finnish historical
newspaper collection Digi. Experiments, results and discussion of this research
serve development of the Web collection of historical Finnish newspapers.
  Digi collection contains 1,960,921 pages of newspaper material from years
1771-1910 both in Finnish and Swedish. We use only material of Finnish
documents in our evaluation. The OCRed newspaper collection has lots of OCR
errors; its estimated word level correctness is about 70-75 % (Kettunen and
P\"a\"akk\"onen, 2016). Our principal NER tagger is a rule-based tagger of
Finnish, FiNER, provided by the FIN-CLARIN consortium. We show also results of
limited category semantic tagging with tools of the Semantic Computing Research
Group (SeCo) of the Aalto University. Three other tools are also evaluated
briefly.
  This research reports first published large scale results of NER in a
historical Finnish OCRed newspaper collection. Results of the research
supplement NER results of other languages with similar noisy data.
