Improvement in Probabilistic Information Retrieval Model: Rewarding Terms with High Relative Term Frequency

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dc.contributor.advisorHuang, Xiangji
dc.creatorZhu, Runjie
dc.date.accessioned2016-11-25T14:11:44Z
dc.date.available2016-11-25T14:11:44Z
dc.date.copyright2016-06-09
dc.date.issued2016-11-25
dc.date.updated2016-11-25T14:11:44Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractIn this thesis, I propose the relative term frequency to be integrated into traditional probabilistic models, in other words, I introduce a set of three influence functions with the application of relative term frequency to model and enhance the performance of the fundamental probabilistic weighting function, BM25. The study aims to exploit the properties of the combination of relative term frequency and BM25. The extensive experiments and analyses conducted in the thesis are based on six of the TREC official datasets, and the results presented have shown a significant improvement in the retrieval effectiveness. The information retrieval system adopted is built on the Okapi Basic Search System (BSS), which offers a reliable and effective packaged framework to exercise the experiments, and to yield an end-to-end retrieval workflow.
dc.identifier.urihttp://hdl.handle.net/10315/32744
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation science
dc.subject.keywordsBM25
dc.subject.keywordsProbabilistic Model
dc.subject.keywordsRelative Term Frequency
dc.titleImprovement in Probabilistic Information Retrieval Model: Rewarding Terms with High Relative Term Frequency
dc.typeElectronic Thesis or Dissertation

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