Analysing the dimension of mode in translation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10441069" target="_blank" >RIV/00216208:11320/21:10441069 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5281/zenodo.4450014" target="_blank" >http://dx.doi.org/10.5281/zenodo.4450014</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5281/zenodo.4450014" target="_blank" >10.5281/zenodo.4450014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysing the dimension of mode in translation

Popis výsledku v původním jazyce

The present chapter applies text classification to test how well we can distinguish between texts along two dimensions: a text-production dimension that distinguishes between translations and non-translations (where translations also include interpreted texts); and a mode dimension that distinguishes between and spoken and written texts. The chapter also aims to investigate the relationship between these two dimensions. Moreover, it investigates whether the same linguistic features that are derived from variational linguistics contribute to the prediction of mode in both translations and non-translations. The distributional information about these features was used to statistically model variation along the two dimensions. The results show that the same feature set can be used to automatically differentiate translations from non-translations, as well as spoken texts from the written texts. However, language variation along the dimension of mode is stronger than that along the dimension of text production, as classification into spoken and written texts delivers better results. Besides, linguistic features that contribute to the distinction between spoken and written mode are similar in both translated and non-translated language.

Název v anglickém jazyce

Analysing the dimension of mode in translation

Popis výsledku anglicky

The present chapter applies text classification to test how well we can distinguish between texts along two dimensions: a text-production dimension that distinguishes between translations and non-translations (where translations also include interpreted texts); and a mode dimension that distinguishes between and spoken and written texts. The chapter also aims to investigate the relationship between these two dimensions. Moreover, it investigates whether the same linguistic features that are derived from variational linguistics contribute to the prediction of mode in both translations and non-translations. The distributional information about these features was used to statistically model variation along the two dimensions. The results show that the same feature set can be used to automatically differentiate translations from non-translations, as well as spoken texts from the written texts. However, language variation along the dimension of mode is stronger than that along the dimension of text production, as classification into spoken and written texts delivers better results. Besides, linguistic features that contribute to the distinction between spoken and written mode are similar in both translated and non-translated language.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

60203 - Linguistics

Projekt

—

Návaznosti

—

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Empirical studies in translation and discourse

ISBN

978-3-96110-301-0

Počet stran výsledku

21

Strana od-do

221-241

Počet stran knihy

246

Název nakladatele

Language Science Press

Místo vydání

Berlin

Kód UT WoS kapitoly

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