Attributing authorship in the noisy digitized correspondence of Jacob and Wilhelm Grimm

doi:10.3389/fdigh.2018.00004

Attributing authorship in the noisy digitized correspondence of Jacob and Wilhelm Grimm

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Attributing authorship in the noisy digitized correspondence of Jacob and Wilhelm Grimm

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dc.contributor.author	Franzini, Greta	pl
dc.contributor.author	Kestemont, Mike	pl
dc.contributor.author	Rotari, Gabriela	pl
dc.contributor.author	Jander, Melina	pl
dc.contributor.author	Ochab, Jeremi [SAP14013682]	pl
dc.contributor.author	Franzini, Emily	pl
dc.contributor.author	Byszuk, Joanna	pl
dc.contributor.author	Rybicki, Jan [SAP13006621]	pl
dc.date.accessioned	2018-10-24T12:02:25Z
dc.date.available	2018-10-24T12:02:25Z
dc.date.issued	2018	pl
dc.identifier.uri	https://ruj.uj.edu.pl/xmlui/handle/item/58613
dc.language	eng	pl
dc.rights	Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/pl/legalcode	*
dc.title	Attributing authorship in the noisy digitized correspondence of Jacob and Wilhelm Grimm	pl
dc.type	JournalArticle	pl
dc.description.additional	Publikacja w j. węgierskim: Szerzőazonosítás Jacob és Wilhelm Grimm zajos, digitalizált levelezésében, Digitális Bölcsészet vol. 5 (2021). DOI: 10.31400/dh-hun.2021.5.3144, https://ruj.uj.edu.pl/xmlui/handle/item/288895	pl
dc.abstract.en	This article presents the results of a multidisciplinary project aimed at better understanding the impact of different digitization strategies in computational text analysis. More specifically, it describes an effort to automatically discern the authorship of Jacob and Wilhelm Grimm in a body of uncorrected correspondence processed by HTR (Handwritten Text Recognition) and OCR (Optical Character Recognition), reporting on the effect this noise has on the analyses necessary to computationally identify the different writing style of the two brothers. In summary, our findings show that OCR digitization serves as a reliable proxy for the more painstaking process of manual digitization, at least when it comes to authorship attribution. Our results suggest that attribution is viable even when using training and test sets from different digitization pipelines. With regard to HTR, this research demonstrates that even though automated transcription significantly increases risk of text misclassification when compared to OCR, a cleanliness above $\approx$ 20% is already sufficient to achieve a higher-than-chance probability of correct binary attribution.	pl
dc.description.volume	5	pl
dc.identifier.doi	10.3389/fdigh.2018.00004	pl
dc.identifier.eissn	2297-2668	pl
dc.title.journal	Frontiers in Digital Humanities	pl
dc.language.container	eng	pl
dc.affiliation	Wydział Filologiczny : Instytut Filologii Angielskiej	pl
dc.affiliation	Wydział Fizyki, Astronomii i Informatyki Stosowanej : Instytut Fizyki im. Mariana Smoluchowskiego	pl
dc.subtype	Article	pl
dc.identifier.articleid	4	pl
dc.rights.original	CC-BY; otwarte czasopismo; ostateczna wersja wydawcy; w momencie opublikowania; 0	pl
dc.identifier.project	ROD UJ / OP	pl