On the features of translationese.
Much research in translation studies indicates that translated texts are ontologically different from original non-translated ones. Translated texts, in any language, can be considered a dialect of that language, known as 'translationese'. Several characteristics of translationese have been proposed...
| Publicado en: | Digital Scholarship in the Humanities pp. 98 - 119 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
04/01/2015
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| Materias: | |
| 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=108489937&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 108489937 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: 04/01/2015 pid: 622 pub: Oxford University Press / USA artinfo: ui: 108489937 10.1093/llc/fqt031 ppf: 98 ppct: 21 formats: fmt: @attributes: type: P size: 11MB tig: atl: On the features of translationese. aug: au: Volansky, Vered Ordan, Noam Wintner, Shuly affil: Department of Computer Science, University of Haifa, Haifa, Israel Institut fur Angewandte Sprachwissenschaft sowie Ubersetzen und Dolmetschen, Universitat des Saarlandes, Saarbriicken, Germany su: Translating & interpreting in literature Machine learning Translating & interpreting Translations Artificial intelligence Machine theory sug: subj: Translating & interpreting in literature Machine learning Translating & interpreting Translations Artificial intelligence Machine theory ab: Much research in translation studies indicates that translated texts are ontologically different from original non-translated ones. Translated texts, in any language, can be considered a dialect of that language, known as 'translationese'. Several characteristics of translationese have been proposed as universal in a series of hypotheses. In this work, we test these hypotheses using a computational methodology that is based on supervised machine learning. We define several classifiers that implement various linguistically informed features, and assess the degree to which different sets of features can distinguish between translated and original texts. We demonstrate that some feature sets are indeed good indicators of translationese, thereby corroborating some hypotheses, whereas others perform much worse (sometimes at chance level), indicating that some 'universal' assumptions have to be reconsidered. 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: 2015 holdings: @attributes: islocal: N |
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