Overview
- New, efficient algorithm for robust dependency parsing
- Extensive empirical evaluation of memory-based dependency parsing
- One-stop reference to dependency-based parsing of natural language
Part of the book series: Text, Speech and Language Technology (TLTB, volume 34)
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About this book
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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Keywords
Table of contents (6 chapters)
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Front Matter
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Back Matter
Reviews
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)
Authors and Affiliations
Bibliographic Information
Book Title: Inductive Dependency Parsing
Authors: Joakim Nivre
Series Title: Text, Speech and Language Technology
DOI: https://doi.org/10.1007/1-4020-4889-0
Publisher: Springer Dordrecht
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Science+Business Media B.V. 2006
Hardcover ISBN: 978-1-4020-4888-3Published: 28 June 2006
Softcover ISBN: 978-90-481-7218-4Published: 18 November 2010
eBook ISBN: 978-1-4020-4889-0Published: 05 August 2006
Series ISSN: 1386-291X
Series E-ISSN: 2542-9388
Edition Number: 1
Number of Pages: XII, 212
Topics: Natural Language Processing (NLP), Linguistics, general, Artificial Intelligence, Computer Appl. in Arts and Humanities, Computational Linguistics, Syntax