Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th 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 language modeling approach to information retrieval
A language modeling approach to information retrieval
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th 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
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Biterm language models for document retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
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Recently, researchers have successfully augmented the language modeling approach with a well-founded framework in order to incorporate relevance feedback. A critical problem in this framework is to estimate a query language model that encodes detailed knowledge about a user's information need. This paper explores several methods for query model estimation, motivated by Zhai's generative model. The generative model is an estimation method that maximizes the generative likelihood of feedback documents according to the estimated query language model. Focusing on some limitations of the original generative model, we propose several estimation methods to resolve these limitations: 1) three-component mixture model, 2) re-sampling feedback documents with document language models, and 3) sampling a relevance document from a relevance document language model. In addition, several hybrid methods are also examined, which combine the query specific smoothing method and the estimated query language model. In experiments, our estimation methods outperform a simple generative model, showing a significant improvement over an initial retrieval.