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Term weighting based on document revision history
Abstract In real-world information retrieval systemIn 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. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term-weighting measure based on raw term frequency alone.measure based on raw term frequency alone.
Abstractsub In real-world information retrieval systemIn 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. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term-weighting measure based on raw term frequency alone.measure based on raw term frequency alone.
Bibtextype article  +
Doi 10.1002/asi.21597  +
Has author Sérgio Nunes + , Cristina Ribeiro + , Gabriel David +
Has extra keyword Collection of documents + , Document collection + , Gold standards + , Social bookmarking + , Term Frequency + , Term weighting + , Wikipedia + , Experiments + , Information retrieval systems + , Search engine + , Information retrieval +
Issn 15322882  +
Issue 12  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 2471–2478  +
Published in Journal of the American Society for Information Science and Technology +
Title Term weighting based on document revision history +
Type literature review  +
Volume 62  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 8 November 2014 06:24:49  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Literature reviews  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 06:24:49  +
DateThis property is a special property in this wiki. 2011  +
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