The purpose of this paper is to establish a new approach for solving the expansion term problem.
This study develops an expansion term weighting function derived from the valuable concepts used by previous approaches. These concepts include probability measurement, adjustment according to situations, and summation of weights. Formal tests have been conducted to compare the proposed weighting function with the baseline ranking model and other weighting functions.
The results reveal stable performance by the proposed expansion term weighting function. It proves more effective than the baseline ranking model and outperforms other weighting functions.
The paper finds that testing additional data sets and potential applications to real working situations is required before the generalisability and superiority of the proposed expansion term weighting function can be asserted.
Stable performance and an acceptable level of effectiveness for the proposed expansion term weighting function indicate the potential for further study and development of this approach. This would add to the current methods studied by the information retrieval community for culling information from documents.
Chou, S., Cheng, C. and Huang, S. (2011), "A ranking algorithm for query expansion based on the term's appearing probability in the single document", Online Information Review, Vol. 35 No. 2, pp. 217-236. https://doi.org/10.1108/14684521111128014Download as .RIS
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