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

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

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Title: Improvement in Probabilistic Information Retrieval Model: Rewarding Terms with High Relative Term Frequency
Author: Zhu, Runjie
Abstract: In 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.
Subject: Information science
Keywords: BM25
Probabilistic Model
Relative Term Frequency
Type: Electronic Thesis or Dissertation
Rights: Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
URI: http://hdl.handle.net/10315/32744
Supervisor: Huang, Xiangji
Degree: MA - Master of Arts
Program: Information Systems and Technology
Exam date: 2016-06-09
Publish on: 2016-11-25

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