Modern tools for old content-in search of named entities in a finnish ocred historical newspaper collection 1771-1910

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationLernen, Wissen, Daten, Analysen 2016
Subtitle of host publicationProceedings of the Conference "Lernen, Wissen, Daten, Analysen", Potsdam, Germany, September 12-14, 2016
EditorsRalf Krestel, Davide Mottin, Emmanuel Müller
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventLernen, Wissen, Daten, Analysen - Potsdam, Germany
Duration: 12 Sep 201614 Sep 2016

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR
Volume1670
ISSN (Print)1613-0073
ISSN (Electronic)1613-0073

Conference

ConferenceLernen, Wissen, Daten, Analysen
CountryGermany
CityPotsdam
Period12/09/201614/09/2016

Researchers

Research units

  • National Library of Finland
  • University of Helsinki

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 [1]. 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 trials and evaluation of NER with data out of a digitized Finnish historical newspaper collection Digi. 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 74-75 % [2]. 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. FiNER is able to achieve up to 60.0 F-score with named entities in the evaluation data. Seco's tools achieve 30.0-60.0 F-score with locations and persons. Performance of FiNER and SeCo's tools with the data shows that at best about half of named entities can be recognized even in a quite erroneous OCRed text.

    Research areas

  • Finnish, Historical Newspaper Collections, Named Entity Recognition

ID: 19095616