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Named Entity Recognition (NER), search, classification and tagging of namesand name like frequent informational elements in texts, has become a standardinformation extraction procedure for textual data. NER has been applied to manytypes of texts and different types of entities: newspapers, fiction, historicalrecords, persons, locations, chemical compounds, protein families, animals etc.In general a NER system's performance is genre and domain dependent and alsoused entity categories vary (Nadeau and Sekine, 2007). The most general set ofnamed entities is usually some version of three partite categorization oflocations, persons and organizations. In this paper we report first large scaletrials and evaluation of NER with data out of a digitized Finnish historicalnewspaper collection Digi. Experiments, results and discussion of this researchserve development of the Web collection of historical Finnish newspapers.  Digi collection contains 1,960,921 pages of newspaper material from years1771-1910 both in Finnish and Swedish. We use only material of Finnishdocuments in our evaluation. The OCRed newspaper collection has lots of OCRerrors; its estimated word level correctness is about 70-75 % (Kettunen andP\"a\"akk\"onen, 2016). Our principal NER tagger is a rule-based tagger ofFinnish, FiNER, provided by the FIN-CLARIN consortium. We show also results oflimited category semantic tagging with tools of the Semantic Computing ResearchGroup (SeCo) of the Aalto University. Three other tools are also evaluatedbriefly.  This research reports first published large scale results of NER in ahistorical Finnish OCRed newspaper collection. Results of the researchsupplement NER results of other languages with similar noisy data.

Old Content and Modern Tools - Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910