Language Model Adaptation for Relevance Feedback in Information Retrieval

Abstract

Language model is a popular method of exploiting linguistic regularities for document retrieval. To improve retrieval performance, the scheme of relevance feedback is adopted by adjusting the query language model using the information feedback from the retrieved documents. This study presents a new Bayesian learning approach to instantaneous and unsupervised adaptation of language model for adaptive information retrieval. We aim to compensate the domain mismatch between query and documents by adapting the query language model to meet the domains of collected documents. The maximum a posteriori adaptation is executed solely by using the input query without additional collection of adaptation data. The retrieved top N documents are utilized as relevant documents and referred as feedback to estimate mixture of language models for Bayesian document retrieval. The experiments on using TREC datasets show that the proposed method significantly outperforms the other relevance feedback methods.