A nonparametric term weighting method for information retrieval based on measuring the divergence from independence

作者：Kocabas Ilker^*; Dincer Bekir Taner; Karaoglan Bahar

来源：Information Retrieval, 2014, 17(2): 153-176.

DOI：10.1007/s10791-013-9225-4

• Information retrieval
• Nonparametric index term weighting
• Statistical dependence
• Pearson%26apos
• s Chi-Square statistics

In this article, we introduce an out-of-the-box automatic term weighting method for information retrieval. The method is based on measuring the degree of divergence from independence of terms from documents in terms of their frequency of occurrence. Divergence from independence has a well-establish underling statistical theory. It provides a plain, mathematically tractable, and nonparametric way of term weighting, and even more it requires no term frequency normalization. Besides its sound theoretical background, the results of the experiments performed on TREC test collections show that its performance is comparable to that of the state-of-the-art term weighting methods in general. It is a simple but powerful baseline alternative to the state-of-the-art methods with its theoretical and practical aspects.

• 出版日期2014-4