Transition network grammars for natural language analysis
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The use of augmented transition network grammars for the analysis of natural language sentences is described, and structure-building actions associated with the arcs of the grammar network allow for a powerful selectivity which can rule out meaningless analyses and take advantage of semantic information to guide the parsing.Abstract:
The use of augmented transition network grammars for the analysis of natural language sentences is described Structure-building actions associated with the arcs of the grammar network allow for the reordering, restructuring, and copying of constituents necessary to produce deep-structure representations of the type normally obtained from a transformational analysis, and conditions on the arcs allow for a powerful selectivity which can rule out meaningless analyses and take advantage of semantic information to guide the parsing The advantages of this model for natural language analysis are discussed in detail and illustrated by examples An implementation of an experimental parsing system for transition network grammars is briefly describedread more
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References
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Journal ArticleDOI
Aspects of the Theory of Syntax
Ann S. Ferebee,Noam Chomsky +1 more
TL;DR: Methodological preliminaries of generative grammars as theories of linguistic competence; theory of performance; organization of a generative grammar; justification of grammar; descriptive and explanatory theories; evaluation procedures; linguistic theory and language learning.
Book
Aspects of the Theory of Syntax
TL;DR: Generative grammars as theories of linguistic competence as discussed by the authors have been used as a theory of performance for language learning. But they have not yet been applied to the problem of language modeling.
Journal ArticleDOI
An efficient context-free parsing algorithm
TL;DR: In this article, a parsing algorithm which seems to be the most efficient general context-free algorithm known is described, which is similar to both Knuth's LR(k) algorithm and the familiar top-down algorithm.