Ngram and Bayesian Classification of Documents for Topic and Authorship.
Large, real world, data sets have been investigated in the context of Authorship Attribution of real world documents. Ngram measures can be used to accurately assign authorship for long documents such as novels. A number of 5 (authors × 5 (movies) arrays of movie reviews were acquired from the...
| Publicado en: | Literary & Linguistic Computing Vol. 18; no. 4; pp. 423 - 448 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Nov2003
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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=16435451&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 16435451 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02681145 BJ1 jtl: Literary & Linguistic Computing issn: 02681145 maglogo: N pubinfo: dt: Nov2003 vid: 18 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 16435451 10.1093/llc/18.4.423 ppf: 423 ppct: 25 formats: fmt: @attributes: type: P size: 256KB tig: atl: Ngram and Bayesian Classification of Documents for Topic and Authorship. aug: au: Clement, Ross Sharp, David affil: Harrow School of Computer Science, University of Westminster, UK su: Authorship Archives Literature Speech Function words (Grammar) sug: subj: Authorship Archives Literature Speech Function words (Grammar) ab: Large, real world, data sets have been investigated in the context of Authorship Attribution of real world documents. Ngram measures can be used to accurately assign authorship for long documents such as novels. A number of 5 (authors × 5 (movies) arrays of movie reviews were acquired from the Internet Movie Database. Both ngram and naive Bayes classifiers were used to classify along both the authorship and topic (movie) axes. Both approaches yielded similar results, and authorship was as accurately detected, or more accurately detected, than topic. Part of speech tagging and function-word lists were used to investigate the influence of structure on classification tasks on documents with meaning removed but grammatical structure intact. 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: 2003 holdings: @attributes: islocal: N |
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