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            Crossroads between contrastive linguistics, translation studies and machine translation

            TC3 II

            Thumbnail
            Contributor(s)
            Hansen-Schirra, Silvia (editor)
            Czulo, Oliver (editor)
            Collection
            Knowledge Unlatched (KU)
            Language
            English
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            Abstract
            Contrastive Linguistics (CL), Translation Studies (TS) and Machine Translation (MT) have common grounds: They all work at the crossroad where two or more languages meet. Despite their inherent relatedness, methodological exchange between the three disciplines is rare. This special issue touches upon areas where the three fields converge. It results directly from a workshop at the 2011 German Association for Language Technology and Computational Linguistics (GSCL) conference in Hamburg where researchers from the three fields presented and discussed their interdisciplinary work. While the studies contained in this volume draw from a wide variety of objectives and methods, and various areas of overlaps between CL, TS and MT are addressed, the volume is by no means exhaustive with regard to this topic. Further cross-fertilisation is not only desirable, but almost mandatory in order to tackle future tasks and endeavours, and this volume is committed to bringing these three fields even closer together.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/180237
            Keywords
            methodology of translation studies; Adjective; Anaphora (linguistics); Bigram; Denmark; Italy; Machine translation; N-gram; Noun; thema EDItEUR::C Language and Linguistics::CF Linguistics
            DOI
            10.5281/zenodo.1019701
            ISBN
            9783946234982
            Publisher
            Language Science Press
            Publisher website
            http://langsci-press.org/
            Publication date and place
            2017
            Grantor
            • Knowledge Unlatched
            Series
            Translation and Multilingual Natural Language Processing,
            Pages
            214
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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