PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Another look at indirect negative evidence
Alexander Clark and Shalom Lappin
In: Proceedings of the EACL Workshop on Cognitive Aspects of Computational Language Acquisition, 31 March 2009, Athens, Greece.

Abstract

Indirect negative evidence is clearly an important way for learners to constrain overgeneralisation, and yet a good learning theoretic analysis has yet to be provided for this, whether in a PAC or a probabilistic identification in the limit framework. In this paper we suggest a theoretical analysis of indirect negative evidence that allows the presence of ungrammatical strings in the input and also accounts for the relationship between grammaticality/acceptability and probability. Given independently justified assumptions about lower bounds on the probabilities of grammatical strings, we establish that a limited number of membership queries of some strings can be probabilistically simulated.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Natural Language Processing
ID Code:6847
Deposited By:Alexander Clark
Deposited On:08 March 2010