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Return to Efficiency
We propose a method for document ranking that combines a simple document-centric view of text, and fast evaluation strategies that have been developed in connection with the vector space model. The new method defines the importance of a term within a document qualitatively rather than quantitatively, and in doing so reduces the need for tuning parameters. In addition, the method supports very fast query processing, with most of the computation carried out on small integers, and dynamic pruning an effective option. Experiments on a wide range of TREC data show that the new method provides retrieval effectiveness as good as or better than the Okapi BM25 formulation, and variants of language models.
@inproceedings{1076075, author = {Vo Ngoc Anh and Alistair Moffat}, title = {Simplified similarity scoring using term ranks}, 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 = {226--233}, location = {Salvador, Brazil}, doi = {http://doi.acm.org/10.1145/1076034.1076075}, publisher = {ACM Press}, address = {New York, NY, USA}, }  ©2006 Association for Computing Machinery |
