A new method for retrieval based on relative entropy with smoothing

Authors:
Hua Huo;Junqiang Liu;Boqin Feng
Affiliations:
Department of Computer Science, Xi'an Jiaotong University, Xi'an, P.R. China;School of Electronics and Information, Henan University of Science and Technology, Luo yang, P.R. China;Department of Computer Science, Xi'an Jiaotong University, Xi'an, P.R. China
Venue:
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
Year:
2005

Citing 8
Cited 0

A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A hidden Markov model information retrieval system

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval as statistical translation

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A general language model for information retrieval (poster abstract)

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval

Proceedings of the tenth international conference on Information and knowledge management
Bayesian extension to the language model for ad hoc information retrieval

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Language Modeling for Information Retrieval

Language Modeling for Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new method for information retrieval based on relative entropy with different smoothing methods has been presented in this paper. The method builds a query language model and document language models respectively for the query and the documents. We rank the documents according to the relative entropies of the estimated document language models with respect to the estimated query language model. While estimating a document language, the efficiency of the smoothing method is considered, we select three popular and relatively efficient methods to smooth the document language model. The feedback documents are used to estimate a query model by the approach that we assume that the feedback documents are generated by a combined model in which one component is the feedback document language model and the other is the collection language model. Experimental results show that the method is effective and performs better than the basic language modeling approach.