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

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
1. Verfasser: Yoshinaka, Ryo
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
C (programming language)
Grammatical inference
Learning
Mildly context-sensitive grammars
Multiple context-free grammars
Natural language (computers)
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Zusammenfassung: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.
ISSN:0304-3975
1879-2294
DOI:10.1016/j.tcs.2010.12.058