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

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.