R-tfidf, A variety of tf-idf Term Weighting Strategy in Document Categorization

Document Type

Conference Proceeding




Faculty of Computing, Health and Science


School of Computer and Security Science




This article was originally published as: Zhu, D., & Xiao, J. (2011,). R-tfidf, A variety of tf-idf term weighting strategy in document categorization. Paper presented at the International Conference on Semantics, Knowledge, and Grids. Beijing. Original article available here


Term weighting strategy plays an essential role in the areas related to text processing such as text categorization and information retrieval. In such systems, term frequency, inverse document frequency, and document length normalization are important factors to be considered when a term weighting strategy is developed. Term length normalization is proposed to give equal opportunities to retrieve both lengthy documents and shorter ones. However, terms in very short documents that may be useless for users, especially in the scenario of Web information retrieval, could be assigned very high weights, resulting in a situation where shorter documents are ranked higher than lengthy documents that are more relevant to users information needs. In this research, a new R-tfidf term weighting strategy is proposed to alleviate the side effects of document length normalization. Experimental results demonstrate the proposed approach can to some extent improve the performance of text categorization.