Distributional learning of abstract categorial grammars

Authors:
Ryo Yoshinaka;Makoto Kanazawa
Affiliations:
Japan Science and Technology Agency and Hokkaido University;National Institute of Informatics, Japan
Venue:
LACL'11 Proceedings of the 6th international conference on Logical aspects of computational linguistics
Year:
2011

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Abstract

Recent studies on grammatical inference have demonstrated the benefits of the learning strategy called "distributional learning" for context-free and multiple context-free languages. This paper gives a comprehensive view of distributional learning of "context-free" formalisms (roughly in the sense of Courcelle 1987) in terms of abstract categorial grammars, in which existing "context-free" formalisms can be encoded.