Comprehension of the Shakespeare authorship question through deep impostors approach.
This article investigates the authorship question surrounding William Shakespeare's works using a novel approach called the 'Deep Impostor' methodology. The approach uses a set of known impostor texts to analyze the origin of a target text collection. Both the target texts and impostors are divided...
| Publicado en: | Digital Scholarship in the Humanities Vol. 40; no. 1; pp. 308 - 329 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Apr2025
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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=184296845&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 184296845 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Apr2025 vid: 40 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 184296845 10.1093/llc/fqaf009 ppf: 308 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.8MB tig: atl: Comprehension of the Shakespeare authorship question through deep impostors approach. aug: au: Volkovich, Zeev Avros, Renata affil: Software Engineering, Braude College, Karmiel 2161002, Israel su: Shakespeare, William, 1564-1616 Convolutional neural networks Artificial neural networks Transformer models Authorship Algorithms sug: subj: Shakespeare, William, 1564-1616 Convolutional neural networks Artificial neural networks Transformer models Authorship Algorithms keyword: deep impostors approach the Shakespeare authorship question ab: This article investigates the authorship question surrounding William Shakespeare's works using a novel approach called the 'Deep Impostor' methodology. The approach uses a set of known impostor texts to analyze the origin of a target text collection. Both the target texts and impostors are divided into an equal number of word segments. A deep neural network, either a Convolutional Neural Network (CNN) or a pre-trained BERT transformer, is then trained and fine-tuned to differentiate between impostor segments. Once assigned, each target text is transformed into a numerical signal by averaging its segment assignments. The Dynamic Time Warping distance is afterward evaluated between these signals to measure their similarity. The Isolation Forest algorithm identifies outliers within the target text collection for each impostor pair by assigning appropriate scores to each tested text. In the summarizing step, the tested creations are clustered into two groups. In the case of a CNN -based model, the first resulting cluster contains fifteen creations. General evaluations lead to the conclusion to suggest that these are not authored by Shakespeare. The remaining documents are classified as authentic Shakespearean works. 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: 2025 holdings: @attributes: islocal: N |
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