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Grammatical trigrams : a probabilistic model of link grammar

Auteur : John D Lafferty; Daniel D Sleator; Davy Temperley
Éditeur : Pittsburgh, Pa. : School of Computer Science, Carnegie Mellon University, [1992]
Collection : Research paper (Carnegie Mellon University. School of Computer Science), CMU-CS-92-181.
Édition/format :  Livre : AnglaisVoir toutes les éditions et les formats
Résumé :
Abstract: "In this paper we present a new class of language models. This class derives from link grammar, a context-free formalism for the description of natural language. We describe an algorithm for determining maximum-likelihood estimates of the parameters of these models. The language models which we present differ from previous models based on stochastic context-free grammars in that they are highly lexical. In  Lire la suite...
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Détails

Format : Livre
Tous les auteurs / collaborateurs : John D Lafferty; Daniel D Sleator; Davy Temperley
Numéro OCLC : 26847322
Notes : "To appear in Proc. of the 1992 AAAI Fall Symp. on Probabilistic Approaches to Natural Language."
"September 1992."
Description : 10 p. ; 28 cm.
Titre de collection : Research paper (Carnegie Mellon University. School of Computer Science), CMU-CS-92-181.
Responsabilité : John Lafferty, Daniel Sleator, Davy Temperley.

Résumé :

Abstract: "In this paper we present a new class of language models. This class derives from link grammar, a context-free formalism for the description of natural language. We describe an algorithm for determining maximum-likelihood estimates of the parameters of these models. The language models which we present differ from previous models based on stochastic context-free grammars in that they are highly lexical. In particular, they include the familiar n-gram models as a natural subclass. The motivation for considering this class is to estimate the contribution which grammar can make to reducing the relative entropy of natural language."

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Données liées


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