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Title:Some correct error-driven versions of the Constraint Demotion algorithm
Authors:Paul Boersma
Abstract:This paper shows that Error-Driven Constraint Demotion (EDCD), an error-driven learning algorithm proposed by Tesar (1995) for Prince and Smolensky's (1993) version of Optimality Theory, can fail to converge to a totally ranked hierarchy of constraints, unlike the earlier non-error-driven learning algorithms proposed by Tesar and Smolensky (1993). The cause of the problem is found in Tesar's use of 'mark-pooling ties', indicating that EDCD can be repaired by assuming Anttila's (1997) 'permuting ties' instead. Simulations show that totally ranked hierarchies can indeed be found by both this repaired version of EDCD and Boersma's (1998) Minimal Gradual Learning Algorithm.
Type:Paper/tech report
Area/Keywords:Learnability,Language Acquisition,Computation
Article:Version 1