Integration of the dual approaches in the distributional learning of context-free grammars

研究成果: Conference contribution

13 被引用数 (Scopus)

抄録

Recently several "distributional learning algorithms" have been proposed and have made great success in learning different subclasses of context-free grammars. The distributional learning models and exploits the relation between strings and contexts that form grammatical sentences in the language of the learning target. There are two main approaches. One, which we call primal, constructs nonterminals whose language is supposed to be characterized by strings. The other, which we call dual, uses contexts to characterize the language of each nonterminal of the conjecture grammar. This paper shows how those opposite approaches are integrated into single learning algorithms that learn quite rich classes of context-free grammars.

本文言語English
ホスト出版物のタイトルLanguage and Automata Theory and Applications - 6th International Conference, LATA 2012, Proceedings
ページ538-550
ページ数13
DOI
出版ステータスPublished - 2012 3 12
外部発表はい
イベント6th International Conference on Language and Automata Theory and Applications, LATA 2012 - A Coruna, Spain
継続期間: 2012 3 52012 3 9

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7183 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other6th International Conference on Language and Automata Theory and Applications, LATA 2012
国/地域Spain
CityA Coruna
Period12/3/512/3/9

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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