Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Feature-Centric View of Information RetrievalModel Learning

A Feature-Centric View of Information Retrieval: Model Learning [This chapter describes how the parameters and features of feature-based ranking functions can be learned from training data. The discussion begins by describing a number of techniques to estimate the model parameters in such a way that the resulting ranking functions are optimized for a target retrieval metric. The section describes a number of numerical analysis-based approaches, as well as a number of more sophisticated machine learning-inspired approaches, which are often referred to as learning to rank approaches. The chapter then continues by describing effective feature selection strategies for feature-based ranking functions and concludes by describing a new line of research that aims at learning models that are both highly effective, but also very efficient.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Feature-Centric View of Information RetrievalModel Learning

Loading next page...
 
/lp/springer-journals/a-feature-centric-view-of-information-retrieval-model-learning-xQ4x1gOJJI

References (0)

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Springer Berlin Heidelberg
Copyright
© Springer-Verlag Berlin Heidelberg 2011
ISBN
978-3-642-22897-1
Pages
121 –148
DOI
10.1007/978-3-642-22898-8_6
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes how the parameters and features of feature-based ranking functions can be learned from training data. The discussion begins by describing a number of techniques to estimate the model parameters in such a way that the resulting ranking functions are optimized for a target retrieval metric. The section describes a number of numerical analysis-based approaches, as well as a number of more sophisticated machine learning-inspired approaches, which are often referred to as learning to rank approaches. The chapter then continues by describing effective feature selection strategies for feature-based ranking functions and concludes by describing a new line of research that aims at learning models that are both highly effective, but also very efficient.]

Published: Jul 14, 2011

Keywords: Information Retrieval; Average Precision; Ranking Function; Retrieval Model; Mean Average Precision

There are no references for this article.