Tailoring Taxonomies for Efficient Text Categorization and Expert Finding
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Semantic and pragmatic annotation for government information discovery, sharing and collaboration
Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government
Adaptive Visual Clustering for Mixed-Initiative Information Structuring
Proceedings of the Symposium on Human Interface 2009 on ConferenceUniversal Access in Human-Computer Interaction. Part I: Held as Part of HCI International 2009
Large-scale hierarchical text classification without labelled data
Proceedings of the fourth ACM international conference on Web search and data mining
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We propose a hierarchical approach to document categorization that requires no pre-configuration and maps the semantic document space to a predefined taxonomy. The utilization of search engines to train a hierarchical classifier makes our approach more flexible than existing solutions which rely on (human) labeled data and are bound to a specific domain. We show that the structural information given by the taxonomy allows for a context aware construction of search queries and leads to higher tagging accuracy. We test our approach on different benchmark datasets and evaluate its performance on the single- and multi-tag assignment tasks. The experimental results show that our solution is as accurate as supervised classifiers for web page classification and still performs well when categorizing domain specific documents.