Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Finding topic words for hierarchical summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 13th international conference on World Wide Web
Enhancing cluster labeling using wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Optimal meta search results clustering
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A framework for personalized and collaborative clustering of search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Topical clustering of search results
Proceedings of the fifth ACM international conference on Web search and data mining
Constructing task-specific taxonomies for document collection browsing
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Utilizing query change for session search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Search result clustering (SRC) organizes search results into labeled hierarchical structures as an "information lay-of-land", providing users an overview and helping them quickly locate relevant information from piles of search results. Hierarchies built by this process are usually sensitive to query changes. For search sessions with multiple queries, this could be undesirable since it may leave users a seemly random overview and partly diminish the benefits that SRC intents to offer. We propose to integrate external knowledge from Wikipedia when building concept hierarchies to boost their stability for session queries. Our evaluations on both TREC 2010 and 2011 Session tracks demonstrate that the proposed approaches outperform the state-of-the-art hierarchy construction algorithms in stability of search results organization.