Graph-based Natural Language Processing and Information Retrieval

This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval.

Rada Mihalcea (Author), Dragomir Radev (Author)

9780521896139, Cambridge University Press

Hardback, published 11 April 2011

202 pages, 136 b/w illus. 11 tables
23.6 x 15.7 x 1.8 cm, 0.45 kg

'The book is highly recommended to be read not only by upper-level undergraduate and graduate students, but also by experts who are looking for a brief overview of this area. The book aims to enable the readers to gain sufficient understanding of graph-based approaches used in information retrieval and to recognize opportunities for advancing the state of art in natural language processing problems by applications of graph theory.' Korhan Gunel, Zentralblatt MATH

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.

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.

Subject Areas: Natural language & machine translation [UYQL], Computer science [UY], Ethical & social aspects of IT [UBJ]