Using temporal profiles of queries for precision prediction
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Answering general time sensitive queries
Proceedings of the 17th ACM conference on Information and knowledge management
Improving search relevance for implicitly temporal queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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We handle a special category of Web queries, queries containing numeric terms. We call them numeric queries. Motivated by some issues in ranking of numeric queries, we detect numeric sensitive queries by mining from retrieved documents using phrase operator. We also propose features based on numeric terms by extracting reliable numeric terms for each document. Finally, a ranking model is trained for numeric sensitive queries, combining proposed numeric-related features and traditional features. Experiments show that our model can significantly improve relevance for numeric sensitive queries.