Efficient learning of multiple context-free languages with multidimensional substitutability from positive data

Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive data. Generalizing their work, this paper presents a polynomial-time learning algorithm for new s...

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Veröffentlicht in:Theoretical computer science 2011-04, Vol.412 (19), p.1821-1831
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description Recently Clark and Eyraud (2007) [10] have shown that substitutable context-free languages, which capture an aspect of natural language phenomena, are efficiently identifiable in the limit from positive data. Generalizing their work, this paper presents a polynomial-time learning algorithm for new subclasses of multiple context-free languages with variants of substitutability.
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source ScienceDirect Journals (5 years ago - present); EZB-FREE-00999 freely available EZB journals
subjects Algorithms
C (programming language)
Grammatical inference
Learning
Mildly context-sensitive grammars
Multiple context-free grammars
Natural language (computers)
title Efficient learning of multiple context-free languages with multidimensional substitutability from positive data
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