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Title:Learning constraint sub-hierarchies. The Bidirectional Gradual Learning Algorithm
Abstract:The paper proposes a variant of Boersma's Gradual Learning
Algorithm for Stochastic Optimality Theory. While in the
original version the learner is always (or tries to become)
a speaker, I assume that the learner is both speaker and
hearer. This learning theory is applied to the OT system
from Aissen (2000), which was developed to explain the
typology of differential case marking. It can be shown
that the constraint sub-hierarchies that Aissen assumes to
be universal follow from the statistical patterns of
language use that have been uncovered in several corpus
studies, if one adopts the bidirectional learning approach.
The paper finally reports some tentative considerations on
the repercussions of this learning theory for typology and
Type:Paper/tech report
Article:Version 1