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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.Read more
- Brings together graph theory and natural language processing
- Offers extensive overview of NLP and IR methods that rely on graphs
- Provides a detailed description of state-of-the-art methods and many pointers to related research work
Reviews & endorsements
"For the first time, a computational framework that unifies many algorithms and representations from the fields of natural language processing and information retrieval. This book is a comprehensive introduction to both theory and practice."
Giorgio Satta, University of Padua
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- Date Published: April 2011
- format: Hardback
- isbn: 9780521896139
- length: 202 pages
- dimensions: 236 x 157 x 18 mm
- weight: 0.45kg
- contains: 136 b/w illus. 11 tables
- availability: Available
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:
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