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Graph-based Natural Language Processing and Information Retrieval [Hardcover]

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  • Category: Books (Computers)
  • Author:  Mihalcea, Rada, Radev, Dragomir
  • Author:  Mihalcea, Rada, Radev, Dragomir
  • ISBN-10:  0521896134
  • ISBN-10:  0521896134
  • ISBN-13:  9780521896139
  • ISBN-13:  9780521896139
  • Publisher:  Cambridge University Press
  • Publisher:  Cambridge University Press
  • Pages:  202
  • Pages:  202
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-May-2011
  • Pub Date:  01-May-2011
  • SKU:  0521896134-11-MPOD
  • SKU:  0521896134-11-MPOD
  • Item ID: 100201070
  • Seller: ShopSpell
  • Ships in: 2 business days
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  • Delivery by: Apr 02 to Apr 04
  • Notes: Brand New Book. Order Now.
This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval.This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. Readers will come away with a firm understanding of the major methods and applications of these topics that rely on graph-based representations and algorithms.This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. Readers will come away with a firm understanding of the major methods and applications of these topics that rely on graph-based representations and algorithms.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-BasedlŠ
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