Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Challenges in enterprise search
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Term proximity scoring for ad-hoc retrieval on very large text collections
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
An exploration of proximity measures in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Understanding the relationship of information need specificity to search query length
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Generating and Browsing Multiple Taxonomies Over a Document Collection
Journal of Management Information Systems
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Discovering key concepts in verbose queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating verbose query processing techniques
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
PRES: a score metric for evaluating recall-oriented information retrieval applications
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Even though queries received by traditional information retrieval systems are quite short, there are many application scenarios where long natural language queries are more effective. Further, incorporating term position information can help improve results of long queries. However, the techniques for incorporating term position information have been developed for terse queries and hence, can not be directly applied to long queries. Though there exist some methods for performing proximal search for long queries, they are not scalable due to long query response times. We describe an intuitive and simple, yet effective technique that implicitly incorporates term position information for long queries in a scalable manner. Our proposed approach achieves more than 700% faster query response times while maintaining the quality of retrieved results when compared with a state-of-the-art method for performing proximal search for very long queries.