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
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)
Re-ranking model based on document clusters
Information Processing and Management: an International Journal
Improving the retrieval effectiveness of very short queries
Information Processing and Management: an International Journal
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering based on cluster validation
Proceedings of the thirteenth ACM international conference on Information and knowledge management
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
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Re-ranking method based on inter-document distances
Information Processing and Management: an International Journal
Regularizing ad hoc retrieval scores
Proceedings of the 14th ACM international conference on Information and knowledge management
Chinese information retrieval based on terms and relevant terms
ACM Transactions on Asian Language Information Processing (TALIP)
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Chinese document re-ranking based on term distribution and maximal marginal relevance
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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
Computer Speech and Language
Utilizing inter-passage and inter-document similarities for re-ranking search results
Proceedings of the 18th ACM conference on Information and knowledge management
Structural re-ranking with cluster-based retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Utilizing inter-passage and inter-document similarities for reranking search results
ACM Transactions on Information Systems (TOIS)
An expansion and reranking approach for annotation-based image retrieval from Web
Expert Systems with Applications: An International Journal
Cluster-based fusion of retrieved lists
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Re-ranking search results using an additional retrieved list
Information Retrieval
From "identical" to "similar": fusing retrieved lists based on inter-document similarities
Journal of Artificial Intelligence Research
The opposite of smoothing: a language model approach to ranking query-specific document clusters
Journal of Artificial Intelligence Research
Exploiting clustering approaches for image re-ranking
Journal of Visual Languages and Computing
Exploiting pairwise recommendation and clustering strategies for image re-ranking
Information Sciences: an International Journal
Exploring the cluster hypothesis, and cluster-based retrieval, over the web
Proceedings of the 21st ACM international conference on Information and knowledge management
A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback
Information Processing and Management: an International Journal
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This paper proposes a novel document re-ranking approach in information retrieval, which is done by a label propagation-based semi-supervised learning algorithm to utilize the intrinsic structure underlying in the large document data. Since no labeled relevant or irrelevant documents are generally available in IR, our approach tries to extract some pseudo labeled documents from the ranking list of the initial retrieval. For pseudo relevant documents, we determine a cluster of documents from the top ones via cluster validation-based k-means clustering; for pseudo irrelevant ones, we pick a set of documents from the bottom ones. Then the ranking of the documents can be conducted via label propagation. Evaluation on benchmark corpora shows that the approach can achieve significant improvement over standard baselines and performs better than other related approaches.