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Coherence relations across speech and sign language: A comparable corpus study of additive connectives
Stockholm University, Faculty of Humanities, Department of Linguistics, Sign Language.ORCID iD: 0000-0003-2918-108X
(English)In: Languages in Contrast: International Journal for Contrastive Linguistics, ISSN 1387-6759, E-ISSN 1569-9897Article in journal (Refereed) Accepted
Abstract [en]

This paper provides the first contrastive analysis of one type of coherence relation (viz. addition) and its connectives across a sign language (French Belgian Sign Language) and a spoken language (French), both used in the same geographical area. The analysis examines the frequency and types of connectives that can express an additive relation, in order to contrast its “markedness” in the two languages, that is, whether addition is marked by dedicated connectives (e.g., English in addition) or by ambiguous, polyfunctional ones (e.g., English and). Furthermore, we investigate in detail the functions of the most frequent additive connective in each language (namely et ‘and’ and the sign SAME ‘and’, ‘like’), starting from the common observation that most connectives, especially high-frequency ones, are highly polyfunctional. This functional analysis intends to show which functions are compatible with the meaning of addition in spoken and signed discourse. Our results show that, despite a common core of shared discourse functions, the equivalence between et and SAME is only partial and relates to a difference in their semantics (monosemy vs. polysemy).

Keywords [en]
Additive connectives, polyfunctionality, corpus annotation, cross-modal analysis, French, French Belgian Sign Language
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Specific Languages
Research subject
Linguistics
Identifiers
URN: urn:nbn:se:su:diva-173319OAI: oai:DiVA.org:su-173319DiVA, id: diva2:1352884
Available from: 2019-09-20 Created: 2019-09-20 Last updated: 2019-09-20

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Crible, LudivineGabarró-López, Sílvia
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf