Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fast and effective query refinement
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Using terminological feedback for web search refinement: a log-based study
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Parsimonious language models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
The TREC robust retrieval track
ACM SIGIR Forum
Better than the real thing?: iterative pseudo-query processing using cluster-based language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Towards a combined model for search and navigation of annotated documents
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A query model based on normalized log-likelihood
Proceedings of the 18th ACM conference on Information and knowledge management
Concept models for domain-specific search
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Bootstrapping subjectivity detection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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We describe a method for applying parsimonious language models to re-estimate the term probabilities assigned by relevance models. We apply our method to six topic sets from test collections in five different genres. Our parsimonious relevance models (i) improve retrieval effectiveness in terms of MAP on all collections, (ii) significantly outperform their non-parsimonious counterparts on most measures, and (iii) have a precision enhancing effect, unlike other blind relevance feedback methods.