Results 191 to 200 of about 250,954 (220)

On rank correlation in information retrieval evaluation

ACM SIGIR Forum, 2007
Some methods for rank correlation in evaluation are examined and their relative advantages and disadvantages are discussed. In particular, it is suggested that different test statistics should be used for providing additional information about the experiments other that the one provided by statistical significance testing. Kendall's τ is often used for
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Multimodal medical information retrieval with unsupervised rank fusion

Computerized Medical Imaging and Graphics, 2015
Modern medical information retrieval systems are paramount to manage the insurmountable quantities of clinical data. These systems empower health care experts in the diagnosis of patients and play an important role in the clinical decision process.
André, Mourão, Flávio, Martins, João, Magalhães   +2 more
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Discovering Ranking Functions for Information Retrieval

2005
The field of information retrieval deals with finding relevant documents from a large document collection or the World Wide Web in response to a user’s query seeking relevant information. Ranking functions play a very important role in the retrieval performance of such retrieval systems and search engines.
Weiguo Fan, Praveen Pathak
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A probability ranking principle for interactive information retrieval [PDF]

Information Retrieval, 2008
The classical Probability Ranking Principle (PRP) forms the theoretical basis for probabilistic Information Retrieval (IR) models, which are dominating IR theory since about 20 years. However, the assumptions underlying the PRP often do not hold, and its view is too narrow for interactive information retrieval (IIR).
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Robust Neural Information Retrieval: An Adversarial and Out-of-Distribution Perspective

ACM Transactions on Information Systems
Recent advances in neural information retrieval models have significantly enhanced these models’ effectiveness across information retrieval tasks. The robustness of these models, which is essential for ensuring their reliability in practice, has also ...
Yuansan Liu, Ruqing Zhang, Jiafeng Guo, M. de Rijke, Yixing Fan, Xueqi Cheng   +5 more
semanticscholar   +1 more source

Semantic Pre-Alignment and Ranking Learning With Unified Framework for Cross-Modal Retrieval

IEEE transactions on circuits and systems for video technology (Print)
Cross-modal retrieval aims at retrieving highly semantic relevant information among multi-modalities. Existing cross-modal retrieval methods mainly explore the semantic consistency between image and text while rarely consider the rankings of positive ...
Qingrong Cheng, Zhenshan Tan, Keyu Wen, Cheng Chen, X. Gu   +4 more
semanticscholar   +1 more source

Relevance ranking in Geographical Information Retrieval

SIGSPATIAL Special, 2011
The study of relevance is one of the central themes in information science where the concern is to match information objects with expressed information needs of the users. Despite substantial advances in search engines and information retrieval (IR) systems in the past decades, this seemingly intuitive concept of relevance remains to be an illusive one
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Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking

IEEE Transactions on Cybernetics, 2020
This paper introduces a convolutional neural network (CNN) semantic re-ranking system to enhance the performance of sketch-based image retrieval (SBIR).
Luo Wang, Xueming Qian, Yuting Zhang, Jialie Shen, Xiaochun Cao   +4 more
semanticscholar   +1 more source

Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval

ACM Trans. Inf. Syst., 2020
The semantic mismatch between query and document terms—i.e., the semantic gap—is a long-standing problem in Information Retrieval (IR). Two main linguistic features related to the semantic gap that can be exploited to improve retrieval are synonymy and ...
M. Agosti, S. Marchesin, G. Silvello
semanticscholar   +1 more source

Learning to rank for geographic information retrieval

Proceedings of the 6th Workshop on Geographic Information Retrieval, 2010
The task of Learning to Rank is currently getting increasing attention, providind a sound methodology for combining different sources of evidence. The goal is to design and apply machine learning methods to automatically learn a function from training data that can sort documents according to their relevance.
Bruno Martins, Pável Calado
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