A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Formal multiple-bernoulli models for language modeling
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A document-centric approach to static index pruning in text retrieval systems
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Assessing multivariate Bernoulli models for information retrieval
ACM Transactions on Information Systems (TOIS)
Within-document term-based index pruning with statistical hypothesis testing
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Progress in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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The Multiple Bernoulli (MB) Language Model has been generally considered too computationally expensive for practical purposes and superseded by the more efficient multinomial approach. While, the model has many attractive properties, little is actually known about the retrieval effectiveness of the MB model due to its high cost of execution. In this paper, we show how an efficient implementation of this model can be achieved. The resulting method is comparable in terms of efficiency to other standard term matching algorithms (such as the vector space model, BM25 and the multinomial Language Model).