Divergence-from-randomness model
In the field of information retrieval, divergence from randomness, one of the first models, is one type of probabilistic model. It is basically used to test the amount of information carried in the documents. It is based on Harter's 2-Poisson indexing-model. The 2-Poisson model has a hypothesis that the level of the documents is related to a set of documents which contains words occur relatively greater than the rest of the documents. It is not a 'model', but a framework for weighting terms using probabilistic methods, and it has a special relationship for term weighting based on notion of eliteness.
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- enIn the field of information retrieval, divergence from randomness, one of the first models, is one type of probabilistic model. It is basically used to test the amount of information carried in the documents. It is based on Harter's 2-Poisson indexing-model. The 2-Poisson model has a hypothesis that the level of the documents is related to a set of documents which contains words occur relatively greater than the rest of the documents. It is not a 'model', but a framework for weighting terms using probabilistic methods, and it has a special relationship for term weighting based on notion of eliteness.
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- enIn the field of information retrieval, divergence from randomness, one of the first models, is one type of probabilistic model. It is basically used to test the amount of information carried in the documents. It is based on Harter's 2-Poisson indexing-model. The 2-Poisson model has a hypothesis that the level of the documents is related to a set of documents which contains words occur relatively greater than the rest of the documents. It is not a 'model', but a framework for weighting terms using probabilistic methods, and it has a special relationship for term weighting based on notion of eliteness. Term weights are being treated as the standard of whether a specific word is in that set or not. Term weights are computed by measuring the divergence between a term distribution produced by a random process and the actual term distribution. Divergence from randomness models set up by instantiating the three main components of the framework: first selecting a basic randomness model, then applying the first normalization and at last normalizing the term frequencies.The basic models are from the following tables.
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- enDivergence-from-randomness model
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- theses.gla.ac.uk/1570/1/2003amatiphd.pdf
- ieomsociety.org/ieom2014/pdfs/513.pdf
- www.is.informatik.uni-duisburg.de/bib/pdf/ir/Abolhassani_Fuhr_04.pdf
- terrier.org/docs/v3.5/dfr_description.html
- ir.dcs.gla.ac.uk/wiki/DivergenceFromRandomness
- agoldst.github.io/dfrtopics/introduction.html
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- Category:Information retrieval techniques
- Category:Probabilistic models
- Category:Ranking functions
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- Information retrieval
- Okapi BM25
- Probabilistic
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- Divergence-from-randomness model
- m.05xxtr
- Q5283894
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- Category:Information retrieval techniques
- Category:Probabilistic models
- Category:Ranking functions
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