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
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Web document clustering: a feasibility demonstration
Proceedings of the 21st 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
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th 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
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating document clustering for interactive information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
The effectiveness of query-specific hierarchic clustering in information retrieval
Information Processing and Management: an International Journal
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
A survey on the use of relevance feedback for information access systems
The Knowledge Engineering Review
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing)
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Representing clusters for retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Inter-document similarities, language models, and ad hoc information retrieval
Inter-document similarities, language models, and ad hoc 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
The opposite of smoothing: a language model approach to ranking query-specific document clusters
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
The opposite of smoothing: a language model approach to ranking query-specific document clusters
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Re-ranking search results using language models of query-specific clusters
Information Retrieval
Inducing word senses to improve web search result clustering
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Clustering web search results with maximum spanning trees
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
The opposite of smoothing: a language model approach to ranking query-specific document clusters
Journal of Artificial Intelligence Research
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
The optimum clustering framework: implementing the cluster hypothesis
Information Retrieval
Query-performance prediction and cluster ranking: two sides of the same coin
Proceedings of the 21st ACM international conference on Information and knowledge management
Ranking document clusters using markov random fields
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
The cluster hypothesis for entity oriented search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A novel neighborhood based document smoothing model for information retrieval
Information Retrieval
Composite retrieval of heterogeneous web search
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
To improve the precision at the very top ranks of a document list presented in response to a query, researchers suggested to exploit information induced from clustering of documents highly ranked by some initial search. We propose a novel model for ranking such (query-specific) clusters by the presumed percentage of relevant documents that they contain. The model is based on (i) proposing a palette of "witness" cluster properties that purportedly correlate with this percentage, (ii) devising concrete quantitative measures for these properties, and (iii) ordering the clusters via aggregation of rankings induced by these individual measures. Empirical evaluation shows that our model is consistently more effective than previously suggested methods in detecting clusters containing a high relevant-document percentage. Furthermore, the precision-at-top-ranks performance of this model transcends that of standard document-based retrieval, and competes with that of a state-of-the-art document-based retrieval approach.