OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Placing search in context: the concept revisited
Proceedings of the 10th international conference on World Wide Web
Automatic web search query generation to create minority language corpora
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using web helper agent profiles in query generation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Probabilistic model for contextual retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge-based query expansion to support scenario-specific retrieval of medical free text
Proceedings of the 2005 ACM symposium on Applied computing
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Construction of query concepts based on feature clustering of documents
Information Retrieval
The role of knowledge in conceptual retrieval: a study in the domain of clinical medicine
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Regularized estimation of mixture models for robust pseudo-relevance feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Adapting ranking SVM to document retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Concept-based biomedical text retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Question processing and clustering in INDOC: a biomedical question answering system
EURASIP Journal on Bioinformatics and Systems Biology
A few examples go a long way: constructing query models from elaborate query formulations
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
MedSearch: a specialized search engine for medical information retrieval
Proceedings of the 17th ACM conference on Information and knowledge management
Incremental mining of information interest for personalized web scanning
Information Systems
Meeting medical terminology needs-the ontology-enhanced Medical Concept Mapper
IEEE Transactions on Information Technology in Biomedicine
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Natural language descriptions are helpful for users to precisely describe medical information needs. However search engines often operate on keyword-based queries. Generating keyword-based queries from the descriptions is thus essential. Its goal lies in retrieving more relevant information that may be ranked high for easy access. In response to the goal, we present a technique MQG (Medical Query Generator) that, given an information need description, generates a query by selecting (from the description) those terms having stronger correlation to medical categories. Empirical evaluation on a medical text database OHSUMED shows that MQG greatly outperforms several state-of-the-art techniques, including those that expand queries by a complete dictionary of medical terms and their equivalence terms in retrieval. Moreover, it reduces the load incurred to the text ranker by retrieving fewer documents for ranking. It also reduces the load incurred to the search engines by using fewer terms in the queries.