Rada Mihalcea

Graph-based Natural Language Processing and Information Retrieval (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 11. April 2011
• Englisch
• ISBN-13: 9781139064491
• Artikelnr.: 38277166

Autorenporträt

Rada Mihalcea is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, where she leads the Language and Information Technologies research group. In 2009, she received the Presidential Early Career Award for Scientists and Engineers, awarded by President Barack Obama. She served on the editorial board of several journals, including Computational Linguistics, the Journal of Natural Language Engineering and Language Resources and Evaluations, and she co-chaired the Empirical Methods in Natural Language Processing conference in 2009 and the Association for Computational Linguistics conference in 2011. She has been published in IEEE Intelligent Systems, the Journal of Natural Language Engineering, the Journal of Machine Translation, Computational Intelligence, the International Journal of Semantic Computing and Artificial Intelligence Magazine.

Inhaltsangabe

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.