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Term weighting based on document revision history

2011; Wiley; Volume: 62; Issue: 12 Linguagem: Inglês

10.1002/asi.21597

1532-2890

Sérgio Nunes, Cristina Ribeiro, Gabriel David,

Information Retrieval and Search Behavior

Journal of the American Society for Information Science and TechnologyVolume 62, Issue 12 p. 2471-2478 Research Article Term weighting based on document revision history Sérgio Nunes, Sérgio Nunes [email protected] INESC Porto, DEI, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n. 4200-465 Porto, PortugalSearch for more papers by this authorCristina Ribeiro, Cristina Ribeiro [email protected] INESC Porto, DEI, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n. 4200-465 Porto, PortugalSearch for more papers by this authorGabriel David, Gabriel David [email protected] INESC Porto, DEI, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n. 4200-465 Porto, PortugalSearch for more papers by this author Sérgio Nunes, Sérgio Nunes [email protected] INESC Porto, DEI, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n. 4200-465 Porto, PortugalSearch for more papers by this authorCristina Ribeiro, Cristina Ribeiro [email protected] INESC Porto, DEI, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n. 4200-465 Porto, PortugalSearch for more papers by this authorGabriel David, Gabriel David [email protected] INESC Porto, DEI, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n. 4200-465 Porto, PortugalSearch for more papers by this author First published: 20 September 2011 https://doi.org/10.1002/asi.21597Citations: 6Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract In real-world information retrieval systems, the underlying document collection is rarely stable or definitive. This work is focused on the study of signals extracted from the content of documents at different points in time for the purpose of weighting individual terms in a document. The basic idea behind our proposals is that terms that have existed for a longer time in a document should have a greater weight. We propose 4 term weighting functions that use each document's history to estimate a current term score. To evaluate this thesis, we conduct 3 independent experiments using a collection of documents sampled from Wikipedia. In the first experiment, we use data from Wikipedia to judge each set of terms. In a second experiment, we use an external collection of tags from a popular social bookmarking service as a gold standard. In the third experiment, we crowdsource user judgments to collect feedback on term preference. 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