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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Kettunen, Kimmo Mäkelä, Eetu Kuokkala, Juha Ruokolainen, Teemu Niemi, Jyrki
dc.contributor.editor Krestel, Ralf
dc.contributor.editor Mottin, Davide
dc.contributor.editor Müller, Emmanuel 2018-12-10T10:10:06Z 2018-12-10T10:10:06Z 2016
dc.identifier.citation Kettunen , K , Mäkelä , E , Kuokkala , J , Ruokolainen , T & Niemi , J 2016 , Modern tools for old content-in search of named entities in a finnish ocred historical newspaper collection 1771-1910 . in R Krestel , D Mottin & E Müller (eds) , Lernen, Wissen, Daten, Analysen 2016 : Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", Potsdam, Germany, September 12-14, 2016 . CEUR Workshop Proceedings , vol. 1670 , CEUR , CEUR WORKSHOP PROCEEDINGS , pp. 124-135 , Lernen, Wissen, Daten, Analysen , Potsdam , Germany , 12/09/2016 . en
dc.identifier.issn 1613-0073
dc.identifier.other PURE UUID: 1538154b-2a72-4e1e-8357-061e6f4c9961
dc.identifier.other PURE ITEMURL:
dc.identifier.other PURE LINK:
dc.identifier.other PURE LINK:
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dc.description.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. en
dc.format.extent 12
dc.format.extent 124-135
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher CEUR
dc.relation.ispartof Lernen, Wissen, Daten, Analysen en
dc.relation.ispartofseries Lernen, Wissen, Daten, Analysen 2016 en
dc.relation.ispartofseries CEUR Workshop Proceedings en
dc.relation.ispartofseries Volume 1670 en
dc.rights openAccess en
dc.subject.other Computer Science(all) en
dc.subject.other 113 Computer and information sciences en
dc.title Modern tools for old content-in search of named entities in a finnish ocred historical newspaper collection 1771-1910 en
dc.type A4 Artikkeli konferenssijulkaisussa fi
dc.description.version Peer reviewed en
dc.contributor.department National Library of Finland
dc.contributor.department Department of Computer Science
dc.contributor.department University of Helsinki
dc.contributor.department Department of Signal Processing and Acoustics
dc.subject.keyword Finnish
dc.subject.keyword Historical Newspaper Collections
dc.subject.keyword Named Entity Recognition
dc.subject.keyword Computer Science(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201812105938
dc.type.version publishedVersion

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