Inductive Dependency Parsing (Text, Speech and Language Technology) - Info and Reading Options
By Joakim Nivre
"Inductive Dependency Parsing (Text, Speech and Language Technology)" is published by Springer in July 28, 2006, it has 216 pages and the language of the book is English.
“Inductive Dependency Parsing (Text, Speech and Language Technology)” Metadata:
- Title: ➤ Inductive Dependency Parsing (Text, Speech and Language Technology)
- Author: Joakim Nivre
- Language: English
- Number of Pages: 216
- Publisher: Springer
- Publish Date: July 28, 2006
“Inductive Dependency Parsing (Text, Speech and Language Technology)” Subjects and Themes:
- Subjects: ➤ Comparative and general Grammar - Linguistics - Computational linguistics - Translators (Computer programs) - Information networks - Artificial intelligence - Syntax - Parsing (Computer grammar) - Dependency grammar - Parsing - Natural language processing (computer science) - Information systems - Linguistics (general) - Artificial Intelligence (incl. Robotics) - Language Translation and Linguistics - Computer Appl. in Arts and Humanities
Edition Specifications:
- Format: Hardcover
- Weight: 1 pounds
- Dimensions: 9.5 x 6.4 x 0.7 inches
Edition Identifiers:
- The Open Library ID: OL8372024M - OL4045220W
- Library of Congress Control Number (LCCN): 2007451140
- ISBN-13: 9781402048883
- ISBN-10: 1402048882
- All ISBNs: 1402048882 - 9781402048883
AI-generated Review of “Inductive Dependency Parsing (Text, Speech and Language Technology)”:
"Inductive Dependency Parsing (Text, Speech and Language Technology)" Description:
The Open Library:
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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