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Graph-based natural language processing and information retrieval

By: Contributor(s): Material type: TextTextPublication details: 2011 Cambridge University Press CambridgeDescription: viii, 192 pISBN:
  • 9780521896139
Subject(s): DDC classification:
  • 005.437 M4G7
Summary: 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. (http://www.cambridge.org/gb/knowledge/isbn/item5980387/?site_locale=en_GB)
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Item type Current library Item location Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 3-A / Slot 81 (0 Floor, West Wing) General Stacks 005.437 M4G7 (Browse shelf(Opens below)) Available 173525

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. (http://www.cambridge.org/gb/knowledge/isbn/item5980387/?site_locale=en_GB)

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