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dc.contributor.authorBruza, Peter D.
dc.contributor.authorSong, Dawei
dc.date.accessioned2009-05-25T14:38:10Z
dc.date.available2009-05-25T14:38:10Z
dc.date.issued2003-08
dc.identifier.citationBRUZA, P. D. and SONG, D., 2003. A comparison of various approaches for using probabilistic dependencies in language modeling. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2003). 28 July – 01 August 2003. New York: ACM. pp. 419-420en
dc.identifier.urihttp://hdl.handle.net/10059/342
dc.description.abstractThe goals of this article is to study several estimates of relevance models which will be computed based on differing approaches for incorporating term dependency information. In this way, we hope to shed light on the relative merits of term dependency information, as well as provide a theoretical framework for such investigations.en
dc.description.sponsorshipThe work reported in this paper has been funded in part by the Cooperative Research Centres Program through the Department of the Prime Minister and Cabinet of Australia.en
dc.format.extent122668 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherACMen
dc.relation.ispartofProceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2003)en
dc.rights"© ACM, 2003. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2003) (ISBN 1581136463), (2003) http://doi.acm.org/10.1145/860435.860530"en
dc.subjectProbabilistic tendenciesen
dc.subjectHyperspace analogue to languageen
dc.subjectInformation flowen
dc.titleA comparison of various approaches for using probabilistic dependencies in language modeling.en
dc.typePosters and presentationsen
dc.publisher.urihttp://doi.acm.org/10.1145/860435.860530en


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