H2: Change and acquisition of verbal structures, University of Stuttgart
Team
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Achim Stein (PI)
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Chantal van Dijk (P3 / H2, Project Bridge 1)
Description
This project investigates change in verb classes, focusing on French. As starting points it takes acquisitional and historical findings on semantic and syntactic class-defining properties. The assumption is that, in language change and in acquisition likewise, verb classes are the product of verbal properties and as such secondary entities. This does not come as a surprise, given that the facets of verb meanings are taken to be grammatically relevant, but it opens a new view on how verb classes are acquired and how they change. In acquisition, it accounts for the fact that some verb classes are acquired later than others or that they are never fully acquired, i.e. continue to show variation even when used by adults. In diachrony, it accounts for inconsistencies, i.e., verb classes not changing as a whole but only with respect to some of their properties. Although lexical and textual resources are available for historical French, diachronic changes have not yet been investigated from this perspective. This project aims at filling the gap by concentrating on French verb classes (dative monotransitives) and structural properties (unaccusativity) that are well documented or have been covered by previous historical studies and which therefore constitute a solid basis for the interdisciplinary research in SILPAC. The overarching research question for this project is:
What is the empirical evidence for a verb class to be historically productive, and how can it be related to processing as well as the Tolerance Principle?
Consistent with the approach of SILPAC, this project will investigate if the changes observed in diachrony can be elicited in processing experiments (SILPAC’s key features 1 and 4), if changes arise in the lifespan of individual authors (key feature 2), how lexical and grammatical properties compete as triggers of change (key feature 3), and if historical resources are a quantitative basis for the application of learning models (key feature 4)