Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Generating hierarchical summaries for web searches
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A tutorial on support vector regression
Statistics and Computing
Cluster Analysis
Phrase-based hierarchical clustering of web search results
ECIR'03 Proceedings of the 25th European conference on IR research
Searching semantic data warehouses: models, issues, architectures
Proceedings of the 2nd International Workshop on Semantic Search over the Web
Navigating the topical structure of academic search results via the Wikipedia category network
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Effective organization of web search results can greatly improve the utility of search engine and enhance the quality of search results. However, the organization of search results is difficult because the sub-topics of a query are usually not explicitly given. In this paper, we propose a novel topic-driven search result organization method, which can first detect the sub-topics of a query by finding the coherent Wikipedia concept groups from its search results; then organize these results using a topic-driven clustering algorithm; in the end we score and rank the topics using the support vector regression model. Empirical results show that our method can achieve competitive performance.