Metre as a stylometric feature in Latin hexameter poetry.
This article demonstrates that metre is a privileged indicator of authorial style in classical Latin hexameter poetry. Using only metrical features, classification experiments are performed between the works of six authors using four different machine-learning models. The results showed a pairwise c...
| Publicado en: | Digital Scholarship in the Humanities Vol. 36; no. 4; pp. 999 - 1013 |
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| Formato: | Artículo |
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Oxford University Press / USA
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=153797226&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 153797226 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Dec2021 vid: 36 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 153797226 10.1093/llc/fqaa043 ppf: 999 ppct: 14 formats: fmt: @attributes: type: P size: 776KB tig: atl: Metre as a stylometric feature in Latin hexameter poetry. aug: au: Nagy, Benjamin affil: Department of Classics, Archaeology and Ancient History, The University of Adelaide , Australia su: Latin poetry Magnitude (Mathematics) Sample size (Statistics) Forgery Italy sug: subj: Italy Latin poetry Magnitude (Mathematics) Sample size (Statistics) Forgery ab: This article demonstrates that metre is a privileged indicator of authorial style in classical Latin hexameter poetry. Using only metrical features, classification experiments are performed between the works of six authors using four different machine-learning models. The results showed a pairwise classification accuracy of at least 90% with samples as small as ten lines and no greater than seventy-five lines (up to around 500 words). In a multiclass setting, classification accuracy exceeded 95% for all four algorithms when using eighty-one-line chunks. These sample sizes are an order of magnitude smaller than those typically recommended for BOW ('bag of words') or n -gram approaches, and the reported accuracy is outstanding. Additionally, this article explores the potential for outlier (forgery) detection, or 'one-class classification'. As an example, analysis of the disputed Aldine Additamentum (Sil. Ital. Pun. 8:144–223) concludes (P < 0.0001) that the metrical style differs significantly from that of the rest of the poem. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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