dc.contributor.author | Kocabas, Ilker | |
dc.contributor.author | Dincer, Bekir Taner | |
dc.contributor.author | Karaoglan, Bahar | |
dc.date.accessioned | 2020-11-20T16:18:09Z | |
dc.date.available | 2020-11-20T16:18:09Z | |
dc.date.issued | 2014 | |
dc.identifier.issn | 1386-4564 | |
dc.identifier.issn | 1573-7659 | |
dc.identifier.uri | https://doi.org/10.1007/s10791-013-9225-4 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12809/3490 | |
dc.description | Dincer, Bekir Taner/0000-0002-0660-7239 | en_US |
dc.description | WOS: 000332963700003 | en_US |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | TUBITAK, The Scientific and Technological Research Council of TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [107E192] | en_US |
dc.description.sponsorship | Authors are thankful to anonymous reviewers for their valuable comments and advices that make this a better paper, and also to Craig Macdonald, Giambattista Amati, and Iadh Ounis for their kind helps. Index term weighting by DFI is developed under the project titled "Design of A Statistical Information Retrieval System'', and supported by TUBITAK, The Scientific and Technological Research Council of Turkey, with Project No: 107E192. Any opinions, findings and conclusions or recommendations expressed in this material are the authors' and do not necessarily reflect those of the sponsor. | en_US |
dc.item-language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.item-rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Information Retrieval | en_US |
dc.subject | Nonparametric Index Term Weighting | en_US |
dc.subject | Statistical Dependence | en_US |
dc.subject | Pearson's Chi-Square Statistics | en_US |
dc.title | A nonparametric term weighting method for information retrieval based on measuring the divergence from independence | en_US |
dc.item-type | article | en_US |
dc.contributor.department | MÜ | en_US |
dc.contributor.departmentTemp | [Kocabas, Ilker; Karaoglan, Bahar] Ege Univ, Int Comp Inst, Izmir, Turkey -- [Dincer, Bekir Taner] Mugla Univ, Dept Stat, Mugla, Turkey -- [Dincer, Bekir Taner] Mugla Univ, Dept Comp Engn, Mugla, Turkey | en_US |
dc.identifier.doi | 10.1007/s10791-013-9225-4 | |
dc.identifier.volume | 17 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.startpage | 153 | en_US |
dc.identifier.endpage | 176 | en_US |
dc.relation.journal | Information Retrieval | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |