Author: Bruce Tesar

Publisher: MIT Press

Published: 2000-05-08

Total Pages: 158

ISBN-13: 9780262264884

DOWNLOAD EBOOK

Highlighting the close relationship between linguistic explanation and learnability, Bruce Tesar and Paul Smolensky examine the implications of Optimality Theory (OT) for language learnability. Highlighting the close relationship between linguistic explanation and learnability, Bruce Tesar and Paul Smolensky examine the implications of Optimality Theory (OT) for language learnability. They show how the core principles of OT lead to the learning principle of constraint demotion, the basis for a family of algorithms that infer constraint rankings from linguistic forms. Of primary concern to the authors are the ambiguity of the data received by the learner and the resulting interdependence of the core grammar and the structural analysis of overt linguistic forms. The authors argue that iterative approaches to interdependencies, inspired by work in statistical learning theory, can be successfully adapted to address the interdependencies of language learning. Both OT and Constraint Demotion play critical roles in their adaptation. The authors support their findings both formally and through simulations. They also illustrate how their approach could be extended to other language learning issues, including subset relations and the learning of phonological underlying forms.

Download or read book Learnability in Optimality Theory written by Bruce Tesar and published by MIT Press. This book was released on 2000-05-08 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highlighting the close relationship between linguistic explanation and learnability, Bruce Tesar and Paul Smolensky examine the implications of Optimality Theory (OT) for language learnability. Highlighting the close relationship between linguistic explanation and learnability, Bruce Tesar and Paul Smolensky examine the implications of Optimality Theory (OT) for language learnability. They show how the core principles of OT lead to the learning principle of constraint demotion, the basis for a family of algorithms that infer constraint rankings from linguistic forms. Of primary concern to the authors are the ambiguity of the data received by the learner and the resulting interdependence of the core grammar and the structural analysis of overt linguistic forms. The authors argue that iterative approaches to interdependencies, inspired by work in statistical learning theory, can be successfully adapted to address the interdependencies of language learning. Both OT and Constraint Demotion play critical roles in their adaptation. The authors support their findings both formally and through simulations. They also illustrate how their approach could be extended to other language learning issues, including subset relations and the learning of phonological underlying forms.