R-tfidf, a Variety of tf-idf Term Weighting Strategy in Document Categorization
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.
Dengya Zhu Jitian XIAO
Digital Dialogue Media Pty Ltd. Level 1, 6-8 Cliff St. Fremantle WA 6160, Australia School of Computer and Security Science, Edith Cowan University Mt Lawley, WA 6050, Australia
国际会议
The Seventh International Conference on Semantics,Knowledge,and Grids(第七届语义、知识与网格国际会议 SKG 20110)
北京
英文
83-90
2011-10-24（万方平台首次上网日期，不代表论文的发表时间）