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Title:Using Inconsistency Detection to Overcome Structural Ambiguity in Language Learning
Authors:Bruce Tesar
Abstract:Using Inconsistency Detection to Overcome Structural Ambiguity in
Language Learning

Bruce Tesar
Rutgers University

This paper proposes the Inconsistency Detection Learner (IDL), an algorithm
for language acquisition intended to address the problem of structural
ambiguity. An overt, acoustically audible form is structurally ambiguous if
different languages admitting the overt form would assign it different
linguistic structural analyses. Because the learner has to be capable of
learning any possible human language, and because the learner is dependent
on overt data to determine what the target language is, the learner must be
capable ultimately of inferring which analysis of an ambiguous overt form is
correct by reference to other overt data of the language. IDL does this in
a particularly direct way, by attempting to construct hypothesis grammars
for combinations of interpretations of the overt forms, and discarding those
combinations that are shown to be inconsistent. A specific implementation
of IDL is given, based on Optimality Theory. Results are presented from a
computational experiment in which this implementation of IDL was applied to
all possible languages predicted by an Optimality theoretic system of
metrical stress grammars. The experimental results show that this learning
algorithm learns quite efficiently for languages from this system,
completely avoiding the potential combinatoric growth in combinations of
interpretations, and suggesting that this approach may play an important
role in the acquisition mechanisms of human learners.
Type:Paper/tech report
Article:Version 1