VAGUE: a user interface to relational databases that permits vague queries
ACM Transactions on Information Systems (TOIS)
Information retrieval using a singular value decomposition model of latent semantic structure
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Using LSI for text classification in the presence of background text
Proceedings of the tenth international conference on Information and knowledge management
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
KDD CUP-2005 report: facing a great challenge
ACM SIGKDD Explorations Newsletter
Q2C@UST: our winning solution to query classification in KDDCUP 2005
ACM SIGKDD Explorations Newsletter
Classifying search engine queries using the web as background knowledge
ACM SIGKDD Explorations Newsletter
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)
Effective keyword-based selection of relational databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Classification-based resource selection
Proceedings of the 18th ACM conference on Information and knowledge management
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Users seeking information in distributed environments of large numbers of disparate information resources are often burdened with the task of repeating their queries for each and every resource. Invariably, some of the searched resources are more productive (yield more useful documents) than others, and it would undoubtedly be useful to try these resources first. If the environment is federated and a single search tool is used to process the query against all the disparate resources, then a similar issue arises: Which information resources should be searched first, to guarantee that useful answers are streamed to users in a timely fashion. In this paper we propose a solution that incorporates techniques from text classification, machine learning and information retrieval. Given a set of pre-classified information resources and a keyword query, our system suggests a relevance ordering of the resources. The approach has been implemented in prototype form, and initial experimentation has given promising results.