Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Retrieval
Improving Automatic Query Classification via Semi-Supervised Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic classification of Web queries using very large unlabeled query logs
ACM Transactions on Information Systems (TOIS)
Mining web query hierarchies from clickthrough data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Classification-based resource selection
Proceedings of the 18th ACM conference on Information and knowledge management
Clustering queries for better document ranking
Proceedings of the 18th ACM conference on Information and knowledge management
A probabilistic topic model with social tags for query reformulation in informational search
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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Query clustering is crucial for automatically discovering frequently asked queries (FAQs) or most popular topics on a question-answering search engine. Due to the short length of queries, the traditional approaches based on keywords are not suitable for query clustering. This paper describes our attempt to cluster similar queries according to their contents as well as the document click information in the user logs.