Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
A first step towards flexible local feedback for ad hoc retrieval
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
MT-based Japanese-Enlish cross-language IR experiments using the TREC test collections
IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Generic summaries for indexing in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Flexible pseudo-relevance feedback using optimization tables
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Relative and absolute term selection criteria: a comparative study for English and Japanese IR
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Topic prediction based on comparative retrieval rankings
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
The NRRC reliable information access (RIA) workshop
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A review of relevance feedback experiments at the 2003 reliable information access (RIA) workshop.
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Ranking the NTCIR systems based on multigrade relevance
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
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
Estimation and use of uncertainty in pseudo-relevance feedback
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive subjective triggers for opinionated document retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Semi-supervised document retrieval
Information Processing and Management: an International Journal
Pseudo relevance feedback with incremental learning for high level feature detection
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multilingual PRF: english lends a helping hand
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Multilingual pseudo-relevance feedback: performance study of assisting languages
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Query expansion for language modeling using sentence similarities
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
Promoting divergent terms in the estimation of relevance models
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
A cluster based pseudo feedback technique which exploits good and bad clusters
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Improving retrievability with improved cluster-based pseudo-relevance feedback selection
Expert Systems with Applications: An International Journal
Personal ontologies: Generation of user profiles based on the YAGO ontology
Information Processing and Management: an International Journal
A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback
Information Processing and Management: an International Journal
Effective and Robust Query-Based Stemming
ACM Transactions on Information Systems (TOIS)
Hi-index | 0.00 |
Although Pseudo-Relevance Feedback (PRF) is a widely used technique for enhancing average retrieval performance, it may actually hurt performance for around one-third of a given set of topics. To enhance the reliability of PRF, Flexible PRF has been proposed, which adjusts the number of pseudo-relevant documents and/or the number of expansion terms for each topic. This paper explores a new, inexpensive Flexible PRF method, called Selective Sampling, which is unique in that it can skip documents in the initial ranked output to look for more “novel” pseudo-relevant documents. While Selective Sampling is only comparable to Traditional PRF in terms of average performance and reliability, per-topic analyses show that Selective Sampling outperforms Traditional PRF almost as often as Traditional PRF outperforms Selective Sampling. Thus, treating the top P documents as relevant is often not the best strategy. However, predicting when Selective Sampling outperforms Traditional PRF appears to be as difficult as predicting when a PRF method fails. For example, our per-topic analyses show that even the proportion of truly relevant documents in the pseudo-relevant set is not necessarily a good performance predictor.