Affiliation:
1. Department of Computer Science, Georgetown University, Washington DC, USA
Abstract
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.
Funder
National Science Foundation
DARPA Memex program
DARPA under agreement number FA8750-14-2-0226
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
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