Learning conjunctive grammars and contextual binary feature grammars

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Approaches based on the idea generically called distributional learning have been making great success in the algorithmic learning of context-free languages and their extensions. We in this paper show that conjunctive grammars are also learnable by a distributional learning technique. Conjunctive grammars are context-free grammars enhanced with conjunctive rules to extract the intersection of two languages. We also compare our result with the closely related work by Clark et al. (JMLR 2010) on contextual binary feature grammars (CBFGs). Our learner is stronger than theirs. In particular our learner learns every exact CBFG, while theirs does not. Clark et al. emphasized the importance of exact CBFGs but they only conjectured there should be a learning algorithm for exact CBFGs. This paper shows that their conjecture is true.

本文言語English
ホスト出版物のタイトルLanguage and Automata Theory and Applications - 9th International Conference, LATA 2015, Proceedings
編集者Adrian-Horia Dediu, Carlos Martín-Vide, Enrico Formenti, Bianca Truthe
出版社Springer Verlag
ページ623-635
ページ数13
ISBN(電子版)9783319155784
DOI
出版ステータスPublished - 2015
外部発表はい
イベント9th International Conference on Language and Automata Theory and Applications, LATA 2015 - Nice, France
継続期間: 2015 3 22015 3 6

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)
8977
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other9th International Conference on Language and Automata Theory and Applications, LATA 2015
国/地域France
CityNice
Period15/3/215/3/6

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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