New Retrieval Method Based on Relative Entropy for LanguageModeling with Different Smoothing Methods

摘要

A language model for information retrieval is built by using a query language model to generate queries and a document language model to generate documents. The documents are ranked according to the relative entropies of estimated document language models with respect to the estimated query language model. Two popular and relatively efficient smoothing methods, the Jelinek-Mercer method and the Absolute discounting method, are used to smooth the document language model in estimation of the document language. A combined model composed of the feedback document language model and the collection language model is used to estimate the query model. A performacne comparison between the new retrieval method and the existing method with feedback is made, and the retrieval performances of the proposed method with the two different smoothing techniques are evaluated on three Text Retrieval Conference (TREC) data sets. Experimental results show that the method is effective and performs better than the basic language modeling approach ; moreover, the method using the Jelinek-Mercer technique performs better than that using the Absolute discounting technique, and the perfomance is sensitive to the smoothing paramters.