Inductive Dependency Parsing / Text, Speech and Language Technology Bd.34 (PDF)
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:...
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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.
- Autor: Joakim Nivre
- 2006, 2006, 212 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 1402048890
- ISBN-13: 9781402048890
- Erscheinungsdatum: 05.08.2006
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 2.95 MB
- Ohne Kopierschutz
- Vorlesefunktion
From the reviews:
"The book demonstrates Nivre’s impressive ability to explain dependency grammar and dependency parsing clearly and succinctly to a wide audience. … The logical progression and the clarity with which this is done is one of the many strengths of this book. … Get Nivre’s book; read it; and enjoy it! The excellent and thorough reference list alone is worth it, constituting a good ten percent of the book." (Christer Samuelsson, Computational Linguistics, Vol. 33 (2), 2007)
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