ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Query enrichment for web-query classification
ACM Transactions on Information Systems (TOIS)
Automatic classification of Web queries using very large unlabeled query logs
ACM Transactions on Information Systems (TOIS)
Varying approaches to topical web query classification
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Determining the informational, navigational, and transactional intent of Web queries
Information Processing and Management: an International Journal
Information Foraging Theory: Adaptive Interaction with Information
Information Foraging Theory: Adaptive Interaction with Information
Function-based question classification for general QA
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Acquiring knowledge about human goals from Search Query Logs
Information Processing and Management: an International Journal
Incorporating revisiting behaviors into click models
Proceedings of the fifth ACM international conference on Web search and data mining
Labeling queries for a people search engine
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Hierarchical target type identification for entity-oriented queries
Proceedings of the 21st ACM international conference on Information and knowledge management
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In this research, we investigate a methodology to classify automatically Web queries by topic and user intent. Taking a 20,000 plus Web query data set sectioned by topic, we manually classified each query using a three-level hierarchy of user intent. We note that significant differences in user intent across topics. Results show that user intent (informational, navigational, and transactional) varies by topic (15 to 24 percent depending on the category). We then use this manually classified data set to classify searches in a Web search engine query stream automatically, using an exact match followed by n-gram approach. These approaches have the advantage of being implementable in real time for query classification of Web searches. The implications are that a search engine can improve retrieval performance by more effectively identifying the intent underlying user queries.