Learning to rank using smoothing methods for language modeling

Yuan Lin, Hongfei Lin, Kan Xu and Xiaoling Sun

Journal of the American Society for Information Science and Technology, 2013, vol. 64, issue 4, 818-828

Abstract: The central issue in language model estimation is smoothing, which is a technique for avoiding zero probability estimation problem and overcoming data sparsity. There are three representative smoothing methods: Jelinek‐Mercer (JM) method; Bayesian smoothing using Dirichlet priors (Dir) method; and absolute discounting (Dis) method, whose parameters are usually estimated empirically. Previous research in information retrieval (IR) on smoothing parameter estimation tends to select a single value from optional values for the collection, but it may not be appropriate for all the queries. The effectiveness of all the optional values should be considered to improve the ranking performance. Recently, learning to rank has become an effective approach to optimize the ranking accuracy by merging the existing retrieval methods. In this article, the smoothing methods for language modeling in information retrieval (LMIR) with different parameters are treated as different retrieval methods, then a learning to rank approach to learn a ranking model based on the features extracted by smoothing methods is presented. In the process of learning, the effectiveness of all the optional smoothing parameters is taken into account for all queries. The experimental results on the Learning to Rank for Information Retrieval (LETOR) LETOR3.0 and LETOR4.0 data sets show that our approach is effective in improving the performance of LMIR.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/asi.22789

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:64:y:2013:i:4:p:818-828

Ordering information: This journal article can be ordered from
https://doi.org/10.1002/(ISSN)1532-2890

Access Statistics for this article

More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology
Bibliographic data for series maintained by Wiley Content Delivery ().