Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
Finding topic words for hierarchical summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Mining generalised disjunctive association rules
Proceedings of the tenth international conference on Information and knowledge management
Automated faceted reporting for web analytics
Proceedings of the 4th international workshop on Web-scale knowledge representation retrieval and reasoning
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Topic hierarchies are a popular method of summarizing the results obtained in response to a query in various search applications. However, topic hierarchies are rigid when they are pre-defined and somewhat unintuitive when they are dynamically generated by statistical techniques. In this paper, we propose an alternative approach to query disambiguation and result summarization by placing the results in set of contextual dimensions which can be viewed as facets. For the generic search scenario, we illustrate our approach by using three types of contextual dimensions, namely, concepts, features, and specializations. We use NLP techniques and a data mining algorithm to select distinct contexts.