Graph-based natural language processing and information retrieval - Info and Reading Options
By Rada Mihalcea and Dragomir Radev
"Graph-based natural language processing and information retrieval" was published by Cambridge University Press in 2011 - Cambridge, it has 192 pages and the language of the book is English.
“Graph-based natural language processing and information retrieval” Metadata:
- Title: ➤ Graph-based natural language processing and information retrieval
- Authors: Rada MihalceaDragomir Radev
- Language: English
- Number of Pages: 192
- Publisher: Cambridge University Press
- Publish Date: 2011
- Publish Location: Cambridge
“Graph-based natural language processing and information retrieval” Subjects and Themes:
- Subjects: ➤ Natural language processing (Computer science) - Graphical user interfaces (Computer systems) - Information retrieval
Edition Specifications:
- Pagination: viii, 192 pages :
Edition Identifiers:
- The Open Library ID: OL24881527M - OL15976436W
- Online Computer Library Center (OCLC) ID: 671573419 - 865075727
- Library of Congress Control Number (LCCN): 2010044578
- ISBN-13: 9780521896139
- All ISBNs: 9780521896139
AI-generated Review of “Graph-based natural language processing and information retrieval”:
"Graph-based natural language processing and information retrieval" Table Of Contents:
- 1- Machine generated contents note: Part I. Introduction to Graph Theory: 1. Notations, properties, and representations; 2. Graph-based algorithms; Part II. Networks: 3. Random networks; 4. Language networks; Part III. Graph-Based Information Retrieval: 5. Link analysis for the world wide web; 6. Text clustering; Part IV. Graph-Based Natural Language Processing: 7. Semantics; 8. Syntax; 9. Applications.
"Graph-based natural language processing and information retrieval" Description:
The Open Library:
"This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval"-- "Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms"--
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