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On learning discontinuous dependencies from positive data

Abstract : This paper is concerned with learning in the model of Gold the Categorial Dependency Grammars (CDG), which express discontin- uous (non-projective) dependencies. We show that rigid and k-valued CDG (without optional and iterative types) are learnable from strings. In fact, we prove that the languages of dependency nets coding rigid CDGs have finite elasticity, and we show a learning algorithm. As a standard corollary, this result leads to the learnability of rigid or k- valued CDGs (without optional and iterative types) from strings.
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Submitted on : Thursday, May 27, 2010 - 5:50:28 PM
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  • HAL Id : hal-00487058, version 1

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Denis Béchet, Alexandre Dikovsky, Annie Foret, Erwan Moreau. On learning discontinuous dependencies from positive data. 9th conference on Formal Grammar (FG 2004), Aug 2004, Nancy, France. pp.1--16. ⟨hal-00487058⟩

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