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Multidisciplinary Information Retrieval

Overview of attention for book
Cover of 'Multidisciplinary Information Retrieval'

Table of Contents

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    Book Overview
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    Chapter 1 Learning-Based Pseudo-Relevance Feedback for Patent Retrieval
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    Chapter 2 Analyzing Parallelism and Domain Similarities in the MAREC Patent Corpus
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    Chapter 3 Query Enhancement for Patent Prior-Art-Search Based on Keyterm Dependency Relations and Semantic Tags
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    Chapter 4 Analysis and Detection of Web Spam by Means of Web Content
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    Chapter 5 Discovery of Environmental Nodes in the Web
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    Chapter 6 Web Searching with Entity Mining at Query Time
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    Chapter 7 Patent and Norm Exploration with the m2n Knowledge Discovery Suite
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    Chapter 8 Hierarchical Classification of Web Documents by Stratified Discriminant Analysis
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    Chapter 9 PatMedia: Augmenting Patent Search with Content-Based Image Retrieval
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    Chapter 10 Generating Variant Keyword Forms for a Morphologically Complex Language Leads to Successful Information Retrieval with Finnish
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    Chapter 11 Analyzing Query Logs of USPTO Examiners to Identify Useful Query Terms in Patent Documents for Query Expansion in Patent Searching: A Preliminary Study
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    Chapter 12 Multidisciplinary Information Retrieval
Attention for Chapter 5: Discovery of Environmental Nodes in the Web
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3 Mendeley
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Chapter title
Discovery of Environmental Nodes in the Web
Chapter number 5
Book title
Multidisciplinary Information Retrieval
Published by
Springer, Berlin, Heidelberg, July 2012
DOI 10.1007/978-3-642-31274-8_5
Book ISBNs
978-3-64-231273-1, 978-3-64-231274-8
Authors

Anastasia Moumtzidou, Stefanos Vrochidis, Sara Tonelli, Ioannis Kompatsiaris, Emanuele Pianta, Moumtzidou, Anastasia, Vrochidis, Stefanos, Tonelli, Sara, Kompatsiaris, Ioannis, Pianta, Emanuele

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 67%
Student > Bachelor 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Mathematics 1 33%

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