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

TL;DR

This paper presents large-scale evaluation of Named Entity Recognition on a Finnish OCRed historical newspaper collection from 1771-1910, highlighting challenges and results in noisy, historical text data.

Contribution

First large-scale NER evaluation on Finnish OCRed historical newspapers, demonstrating the effectiveness of rule-based and semantic tagging tools in noisy, historical data.

Findings

NER performance varies with OCR quality and text genre

Rule-based Finnish NER (FiNER) achieves promising results

Semantic tagging tools provide additional entity insights

Abstract

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.