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Browsing Hierarchy Construction by Minimum Evolution

Browsing Hierarchy Construction by Minimum Evolution Browsing Hierarchy Construction by Minimum Evolution HUI YANG, Department of Computer Science, Georgetown University Hierarchies serve as browsing tools to access information in document collections. This article explores techniques to derive browsing hierarchies that can be used as an information map for task-based search. It proposes a novel minimum-evolution hierarchy construction framework that directly learns semantic distances from training data and from users to construct hierarchies. The aim is to produce globally optimized hierarchical structures by incorporating user-generated task specifications into the general learning framework. Both an automatic version of the framework and an interactive version are presented. A comparison with state-of-the-art systems and a user study jointly demonstrate that the proposed framework is highly effective. Categories and Subject Descriptors: H.3.2 [Information Storage and Retrieval]: Information Storage General Terms: Algorithms, Performance Additional Key Words and Phrases: Browsing hierarchy construction, information organization, complex search, minimum evolution ACM Reference Format: Hui Yang. 2015. Browsing hierarchy construction by minimum evolution. ACM Trans. Inf. Syst. 33, 3, Article 13 (March 2015), 33 pages. DOI: http://dx.doi.org/10.1145/2714574 1. INTRODUCTION As Web users have become more involved in Web search, search tasks have become more complex and dynamic. However, examining a long list of documents http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Information Systems (TOIS) Association for Computing Machinery

Browsing Hierarchy Construction by Minimum Evolution

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References (57)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
1046-8188
DOI
10.1145/2714574
Publisher site
See Article on Publisher Site

Abstract

Browsing Hierarchy Construction by Minimum Evolution HUI YANG, Department of Computer Science, Georgetown University Hierarchies serve as browsing tools to access information in document collections. This article explores techniques to derive browsing hierarchies that can be used as an information map for task-based search. It proposes a novel minimum-evolution hierarchy construction framework that directly learns semantic distances from training data and from users to construct hierarchies. The aim is to produce globally optimized hierarchical structures by incorporating user-generated task specifications into the general learning framework. Both an automatic version of the framework and an interactive version are presented. A comparison with state-of-the-art systems and a user study jointly demonstrate that the proposed framework is highly effective. Categories and Subject Descriptors: H.3.2 [Information Storage and Retrieval]: Information Storage General Terms: Algorithms, Performance Additional Key Words and Phrases: Browsing hierarchy construction, information organization, complex search, minimum evolution ACM Reference Format: Hui Yang. 2015. Browsing hierarchy construction by minimum evolution. ACM Trans. Inf. Syst. 33, 3, Article 13 (March 2015), 33 pages. DOI: http://dx.doi.org/10.1145/2714574 1. INTRODUCTION As Web users have become more involved in Web search, search tasks have become more complex and dynamic. However, examining a long list of documents

Journal

ACM Transactions on Information Systems (TOIS)Association for Computing Machinery

Published: Mar 23, 2015

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