Names, Right or Wrong: Named Entities in an OCRed Historical Finnish Newspaper Collection

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 [16]. 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 evaluation result 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% [7]. Our baseline NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. Three other available tools are also evaluated: a Finnish Semantic Tagger (FST), Connexor's NER tool and Polyglot's NER.