Results 121 to 130 of about 1,322 (154)
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2011
At the heart of many effective approaches to the core information retrieval problem-identifying relevant content-lies the following three-fold strategy: obtaining content based matches, inferring additional ranking criteria and constraints, and combining all of the above so as to arrive at a single ranking of retrieval units.
Katja Hofmann +2 more
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At the heart of many effective approaches to the core information retrieval problem-identifying relevant content-lies the following three-fold strategy: obtaining content based matches, inferring additional ranking criteria and constraints, and combining all of the above so as to arrive at a single ranking of retrieval units.
Katja Hofmann +2 more
openaire +3 more sources
Multilabel Ranking with Inconsistent Rankers
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014While most existing multilabel ranking methods assume the availability of a single objective label ranking for each instance in the training set, this paper deals with a more common case where subjective inconsistent rankings from multiple rankers are associated with each instance.
Xin Geng, Longrun Luo
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Multiple Ranker Method in Document Retrieval
Communications in Computer and Information Science, 2008In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new ...
Yalou Huang +2 more
exaly +2 more sources
Different Rankers on Different Subcollections
2015Recent work has shown that when documents in a TREC ad hoc collection are partitioned, different rankers will perform optimally on different partitions. This result suggests that choosing different highly effective rankers for each partition and merging the results, should be able to improve overall effectiveness.
Jones, Timothy +4 more
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Robustness of Neural Rankers to Typos: A Comparative Study
Proceedings of the 26th Australasian Document Computing Symposium, 2022Recent advances in passage retrieval have seen the introduction of pre-trained language models (PLMs) based neural rankers. While generally very effective, little attention has been paid to the robustness of these rankers. In this paper, we study the effectiveness of state-of-the-art PLM rankers in presence of typos in queries, as an indication of the ...
Shengyao Zhuang +2 more
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Learning a Fast Bipartite Ranker for Text Documents Using Lexicographical Rankers and ROC Curves
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017The design of powerful learning methods for addressing huge amounts of unstructured data, such as text documents, is a fundamental problem within the document analysis and recognition community. In this work, we propose FlexRank, a specially designed bipartite ranking algorithm for text documents using lexicographical ordering. FlexRank is based on the
Lucas de Souza Rodrigues +2 more
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Active query selection for learning rankers
Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception to this (Long et al. 2010) uses expected loss optimization (ELO) to estimate which queries should be selected but is limited to rankers that predict absolute graded relevance ...
Mustafa Bilgic 0001, Paul N. Bennett
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