Graph-based Natural Language Processing and Information Retrieval by Rada Mihalcea, Dragomir Radev | | Hardcover | Barnes & Noble
Graph-based Natural Language Processing and Information Retrieval

Graph-based Natural Language Processing and Information Retrieval

by Rada Mihalcea, Dragomir R. Radev
     
 

ISBN-10: 0521896134

ISBN-13: 9780521896139

Pub. Date: 04/11/2011

Publisher: Cambridge University Press

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

Overview

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.

Product Details

ISBN-13:
9780521896139
Publisher:
Cambridge University Press
Publication date:
04/11/2011
Edition description:
New Edition
Pages:
208
Product dimensions:
6.20(w) x 9.30(h) x 0.70(d)

Table of Contents

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.

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