The effect of author set size and data size in authorship attribution.
Applications of authorship attribution `in the wild’ [Koppel, M., Schler, J., and Argamon, S. (2010). Authorship attribution in the wild. Language Resources and Evaluation. Advanced Access published January 12, 2010:10.1007/s10579-009-9111-2], for instance in social networks, will likely involve lar...
| Publicado en: | Literary & Linguistic Computing Vol. 26; no. 1; pp. 35 - 56 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Apr2011
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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=59688140&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 59688140 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02681145 BJ1 jtl: Literary & Linguistic Computing issn: 02681145 maglogo: N pubinfo: dt: Apr2011 vid: 26 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 59688140 10.1093/llc/fqq013 ppf: 35 ppct: 21 formats: fmt: @attributes: type: P size: 316KB tig: atl: The effect of author set size and data size in authorship attribution. aug: au: Luyckx, Kim Daelemans, Walter affil: CLiPS Computational Linguistics Group, University of Antwerp, Belgium su: Authorship Social networks Literacy Linguistics sug: subj: Authorship Social networks Literacy Linguistics ab: Applications of authorship attribution `in the wild’ [Koppel, M., Schler, J., and Argamon, S. (2010). Authorship attribution in the wild. Language Resources and Evaluation. Advanced Access published January 12, 2010:10.1007/s10579-009-9111-2], for instance in social networks, will likely involve large sets of candidate authors and only limited data per author. In this article, we present the results of a systematic study of two important parameters in supervised machine learning that significantly affect performance in computational authorship attribution: (1) the number of candidate authors (i.e. the number of classes to be learned), and (2) the amount of training data available per candidate author (i.e. the size of the training data). We also investigate the robustness of different types of lexical and linguistic features to the effects of author set size and data size. The approach we take is an operationalization of the standard text categorization model, using memory-based learning for discriminating between the candidate authors. We performed authorship attribution experiments on a set of three benchmark corpora in which the influence of topic could be controlled. The short text fragments of e-mail length present the approach with a true challenge. Results show that, as expected, authorship attribution accuracy deteriorates as the number of candidate authors increases and size of training data decreases, although the machine learning approach continues performing significantly above chance. Some feature types (most notably character n-grams) are robust to changes in author set size and data size, but no robust individual features emerge. 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: 2011 holdings: @attributes: islocal: N |
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