Graph-based Natural Language Processing and Information Retrieval

Nonfiction, Computers, Advanced Computing, Natural Language Processing, Computer Science, General Computing
Cover of the book Graph-based Natural Language Processing and Information Retrieval by Rada Mihalcea, Dragomir Radev, Cambridge University Press
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Author: Rada Mihalcea, Dragomir Radev ISBN: 9781107386631
Publisher: Cambridge University Press Publication: April 11, 2011
Imprint: Cambridge University Press Language: English
Author: Rada Mihalcea, Dragomir Radev
ISBN: 9781107386631
Publisher: Cambridge University Press
Publication: April 11, 2011
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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