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Inductive Dependency Parsing / by Joakim Nivre.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Text, Speech and Language Technology ; 34Editor: Dordrecht : Springer Netherlands, 2006Descripción: xI, 216 páginas recurso en líneaTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9781402048890
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • P1-1091
Recursos en línea:
Contenidos:
Natural Language Parsing -- Dependency Parsing -- Inductive Dependency Parsing -- Treebank Parsing -- Conclusion.
Resumen: This book provides an in-depth description of the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text. This methodology is based on two essential components: dependency-based syntactic representations and a data-driven approach to syntactic parsing. More precisely, it is based on a deterministic parsing algorithm in combination with inductive machine learning to predict the next parser action. The book includes a theoretical analysis of all central models and algorithms, as well as a thorough empirical evaluation of memory-based dependency parsing, using data from Swedish and English. Offering the reader a one-stop reference to dependency-based parsing of natural language, it is intended for researchers and system developers in the language technology field, and is also suited for graduate or advanced undergraduate education.
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Springer eBooks

Natural Language Parsing -- Dependency Parsing -- Inductive Dependency Parsing -- Treebank Parsing -- Conclusion.

This book provides an in-depth description of the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text. This methodology is based on two essential components: dependency-based syntactic representations and a data-driven approach to syntactic parsing. More precisely, it is based on a deterministic parsing algorithm in combination with inductive machine learning to predict the next parser action. The book includes a theoretical analysis of all central models and algorithms, as well as a thorough empirical evaluation of memory-based dependency parsing, using data from Swedish and English. Offering the reader a one-stop reference to dependency-based parsing of natural language, it is intended for researchers and system developers in the language technology field, and is also suited for graduate or advanced undergraduate education.

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