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• W1669902438 abstract "A central problem for NLP is grammar induction: the development of unsupervised learning algorithms for syntax. In this paper we present a lattice-theoretic representation for natural language syntax, called Distributional Lattice Grammars. These representations are objective or empiricist, based on a generalisation of distributional learning, and are capable of representing all regular languages, some but not all context-free languages and some non-context-free languages. We present a simple algorithm for learning these grammars together with a complete self-contained proof of the correctness and efficiency of the algorithm." @default.
• W1669902438 created "2016-06-24" @default.
• W1669902438 creator A5047100763 @default.
• W1669902438 date "2010-07-15" @default.
• W1669902438 modified "2023-09-22" @default.
• W1669902438 title "Efficient, Correct, Unsupervised Learning for Context-Sensitive Languages" @default.
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