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            Attributive constructions in North-Eastern Neo-Aramaic (Volume 15)

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            Author(s)
            Gutman, Ariel
            Collection
            Knowledge Unlatched (KU)
            Language
            English
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            Abstract
            This study is the first wide-scope morpho-syntactic comparative study of North-Eastern Neo-Aramaic dialects to date. Given the historical depth of Aramaic (almost 3 millennia) and the geographic span of the modern dialects, coming in contact with various Iranian, Turkic and Semitic languages, these dialects provide an almost pristine "laboratory" setting for examining language change from areal, typological and historical perspectives. While the study has a very wide coverage of dialects, including also contact languages (and especially Kurdish dialects), it focuses on a specific grammatical domain, namely attributive constructions, giving a theoretically motivated and empirically grounded account of their variation, distribution and development. The results will be enlightening not only to Semitists seeking to learn about this fascinating modern Semitic language group, but also for typologists and general linguists interested in the dynamics of noun phrase morphosyntax.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/188502
            Keywords
            Language Arts & Disciplines; Linguistics; Historical & Comparative; thema EDItEUR::C Language and Linguistics::CF Linguistics::CFF Historical and comparative linguistics
            DOI
            https://doi.org/10.5281/zenodo.1182527
            ISBN
            9783961100811
            Publisher
            Language Science Press
            Publisher website
            http://langsci-press.org/
            Publication date and place
            2018
            Imprint
            Language Science Press
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            Credits


            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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