Quantitative Authorship Attribution: An Evaluation of Techniques.
The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author's writing sample. Over the past three centuries,...
| Publicado en: | Literary & Linguistic Computing Vol. 22; no. 3; pp. 251 - 271 |
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| Formato: | Artículo |
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Oxford University Press / USA
Sep2007
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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=26863966&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 26863966 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02681145 BJ1 jtl: Literary & Linguistic Computing issn: 02681145 maglogo: N pubinfo: dt: Sep2007 vid: 22 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 26863966 10.1093/llc/fqm020 ppf: 251 ppct: 20 formats: fmt: @attributes: type: P size: 153KB tig: atl: Quantitative Authorship Attribution: An Evaluation of Techniques. aug: au: Grieve, Jack affil: English Department Northern Arizona University su: Attribution of authorship Authorship Authors Algorithms Graphemics Word frequency Punctuation Collocation (Linguistics) sug: subj: Attribution of authorship Authorship Authors Algorithms Graphemics Word frequency Punctuation Collocation (Linguistics) ab: The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author's writing sample. Over the past three centuries, many types of textual measurements have been proposed, but never before have the majority of these measurements been tested on the same dataset. A large-scale comparison of textual measurements is crucial if current techniques are to be used effectively and if new and more powerful techniques are to be developed. This article presents the results of a comparison of thirty-nine different types of textual measurements commonly used in attribution studies, in order to determine which are the best indicators of authorship. Based on the results of these tests, a more accurate approach to quantitative authorship attribution is proposed, which involves the analysis of many different textual measurements. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Literary & Linguistic Computing holder: Oxford University Press / USA dt: @attributes: year: 2007 holdings: @attributes: islocal: N |
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