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.
Rezensionen / Stimmen
'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 '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
Sprache
Verlagsort
Zielgruppe
Illustrationen
11 Tables, unspecified; 3 Halftones, unspecified; 133 Line drawings, unspecified
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 16 mm
Gewicht
ISBN-13
978-0-521-89613-9 (9780521896139)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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. Dragomir Radev is a Professor in the School of Information, the Department of Electrical Engineering and Computer Science, and the Department of Linguistics at the University of Michigan, where he is leader of the Computational Linguistics and Information Retrieval research group (CLAIR). He has more than 100 publications in conferences and journals such as Communications of the ACM, the Journal of Artificial Intelligence Research, Bioinformatics, Computational Linguistics, Information Processing and Management and the American Journal of Political Science, among others. He is on the editorial boards of Information Retrieval, the Journal of Natural Language Engineering and the Journal of Artificial Intelligence Research. Radev is an ACM distinguished scientist as well as the coach of the US high school team in computational linguistics.
Autor*in
University of North Texas
University of Michigan, Ann Arbor
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.
