SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Data mining for hypertext: a tutorial survey
ACM SIGKDD Explorations Newsletter
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Identifying ambiguous queries in web search
Proceedings of the 16th international conference on World Wide Web
Learning latent semantic relations from clickthrough data for query suggestion
Proceedings of the 17th ACM conference on Information and knowledge management
Behavior-driven clustering of queries into topics
Proceedings of the 20th ACM international conference on Information and knowledge management
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The keyword based search technique suffers from the problem of synonymic and polysemic queries. Current approaches address only the problem of synonymic queries in which different queries might have the same information requirement. But the problem of polysemic queries, i.e., same query having different intentions, still remains unaddressed. In this paper, we propose the notion of intent clusters, the members of which will have the same intention. We develop a clustering algorithm that uses the user session information in query logs in addition to query URL entries to identify cluster of queries having the same intention. The proposed approach has been studied through case examples from the actual log data from AOL, and the clustering algorithm is shown to be successful in discerning the user intentions.