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» Parallel learning to rank for information retrieval
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IR
2011
13 years 2 months ago
Learning to rank for why-question answering
In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivate...
Suzan Verberne, Hans van Halteren, Daphne Theijsse...
MM
2004
ACM
152views Multimedia» more  MM 2004»
14 years 1 months ago
Manifold-ranking based image retrieval
In this paper, we propose a novel transductive learning framework named manifold-ranking based image retrieval (MRBIR). Given a query image, MRBIR first makes use of a manifold ra...
Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang...
SIGIR
2008
ACM
13 years 7 months ago
Novelty and diversity in information retrieval evaluation
Evaluation measures act as objective functions to be optimized by information retrieval systems. Such objective functions must accurately reflect user requirements, particularly w...
Charles L. A. Clarke, Maheedhar Kolla, Gordon V. C...
IR
2010
13 years 6 months ago
A general approximation framework for direct optimization of information retrieval measures
Recently direct optimization of information retrieval (IR) measures becomes a new trend in learning to rank. Several methods have been proposed and the effectiveness of them has ...
Tao Qin, Tie-Yan Liu, Hang Li
ECML
2007
Springer
13 years 9 months ago
Sequence Labeling with Reinforcement Learning and Ranking Algorithms
Many problems in areas such as Natural Language Processing, Information Retrieval, or Bioinformatic involve the generic task of sequence labeling. In many cases, the aim is to assi...
Francis Maes, Ludovic Denoyer, Patrick Gallinari