SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Improve retrieval accuracy for difficult queries using negative feedback
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A study of methods for negative relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A unified and discriminative model for query refinement
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A survey of pre-retrieval query performance predictors
Proceedings of the 17th ACM conference on Information and knowledge management
Term proximity scoring for keyword-based retrieval systems
ECIR'03 Proceedings of the 25th European conference on IR research
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Modern search engines usually provide a query language with a set of advanced syntactic operators (e.g., plus sign to require a term's appearance, or quotation marks to require a phrase's appearance) which if used appropriately, can significantly improve the effectiveness of a plain keyword query. However, they are rarely used by ordinary users due to the intrinsic difficulties and users' lack of corpora statistics. In this paper, we propose to automatically reformulate queries that do not work well by selectively adding syntactic operators. Particularly, we propose to perform syntactic operator-based query reformulation when a retrieval system detects users encounter difficulty in search as indicated by users' behaviors such as scanning over top k documents without click-through. We frame the problem of automatic reformulation with syntactic operators as a supervised learning problem, and propose a set of effective features to represent queries with syntactic operators. Experiment results verify the effectiveness of the proposed method and its applicability as a query suggestion mechanism for search engines. As a negative feedback strategy, syntactic operator-based query reformulation also shows promising results in improving search results for difficult queries as compared with existing methods.