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Gradient Acceptability and Linguistic Theory

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Gradient Acceptability and Linguistic Theory

Gradient Acceptability and Linguistic Theory by Elaine J. Francis
English | February 15, 2022 | ISBN: 0192898949, 0192898957 | 288 pages | PDF | 11 MB

This book examines a challenging problem at the intersection of theoretical linguistics and the psychology of language: the interpretation of gradient judgments of sentence acceptability in relation to theories of grammatical knowledge. Acceptability judgments constitute the primary source of
data on which such theories have been built, despite being susceptible to various extra-grammatical factors. Through a review of experimental and corpus-based research on a variety of syntactic phenomena and an in-depth examination of two case studies, Elaine J. Francis argues for two main
positions. The first is that converging evidence from online comprehension tasks, elicited production tasks, and corpora of naturally-occurring discourse can help to determine the sources of variation in acceptability judgments and to narrow down the range of plausible theoretical interpretations.
The second is that the interpretation of judgment data depends crucially on the theoretical commitments and assumptions made, especially with respect to the nature of the syntax-semantics interface and the choice of either a categorical or a gradient notion of grammaticality. The theoretical
frameworks considered in this book include derivational theories (e.g. Minimalism, Principles and Parameters), constraint-based theories (e.g. Sign-based Construction Grammar, Simpler Syntax), competition-based theories (e.g. Stochastic Optimality Theory, Decathlon Model), and usage-based
approaches. The volume shows that while acceptability judgment data are typically compatible with the assumptions of various theoretical frameworks, some gradient phenomena are best captured within frameworks that permit soft constraints-non-categorical grammatical constraints that encode the conventional preferences of language users.

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