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Return to Theory 3
In Information Retrieval (IR), the Dirichlet Priors have been applied to the smoothing technique of the language modeling approach. In this paper, we apply the Dirichlet Priors to the term frequency normalisation of the classical BM25 probabilistic model and the Divergence from Randomness PL2 model. The contributions of this paper are twofold. First, through extensive experiments on four TREC collections, we show that the newly generated models, to which the Dirichlet Priors normalisation is applied, provide robust and effective performance. Second, we propose a novel theoretically-driven approach to the automatic parameter tuning of the Dirichlet Priors normalisation. Experiments show that this tuning approach optimises the retrieval performance of the newly generated Dirichlet Priors-based weighting models.
@inproceedings{1076114, author = {Ben He and Iadh Ounis}, title = {A study of the dirichlet priors for term frequency normalisation}, booktitle = {SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval}, year = {2005}, isbn = {1-59593-034-5}, pages = {465--471}, location = {Salvador, Brazil}, doi = {http://doi.acm.org/10.1145/1076034.1076114}, publisher = {ACM Press}, address = {New York, NY, USA}, }  ©2006 Association for Computing Machinery |
