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ECIR
2010
Springer
13 years 7 months ago
Learning to Select a Ranking Function
Abstract. Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly ...
Jie Peng, Craig Macdonald, Iadh Ounis
BMCBI
2007
146views more  BMCBI 2007»
13 years 6 months ago
PubMed related articles: a probabilistic topic-based model for content similarity
Background: We present a probabilistic topic-based model for content similarity called pmra that underlies the related article search feature in PubMed. Whether or not a document ...
Jimmy J. Lin, W. John Wilbur
IPM
2008
100views more  IPM 2008»
13 years 6 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...
SIGIR
2008
ACM
13 years 6 months ago
A few examples go a long way: constructing query models from elaborate query formulations
We address a specific enterprise document search scenario, where the information need is expressed in an elaborate manner. In our scenario, information needs are expressed using a...
Krisztian Balog, Wouter Weerkamp, Maarten de Rijke
IPM
2007
149views more  IPM 2007»
13 years 6 months ago
Web page title extraction and its application
This paper is concerned with automatic extraction of titles from the bodies of HTML documents (web pages). Titles of HTML documents should be correctly defined in the title fields...
Yewei Xue, Yunhua Hu, Guomao Xin, Ruihua Song, Shu...