Plane of rotation: Difference between revisions

Revision as of 19:40, 6 July 2013

The probabilistic relevance model^[1] was devised by Robertson and Jones as a framework for probabilistic models to come.

It makes an estimation of the probability of finding if a document d_j is relevant to a query q. This model assumes that this probability of relevance depends on the query and document representations. Furthermore, it assumes that there is a portion of all documents that is preferred by the user as the answer set for query q. Such an ideal answer set is called R and should maximize the overall probability of relevance to that user. The prediction is that documents in this set R are relevant to the query, while documents not present in the set are non-relevant.

$s i m (d_{j, q}) = \frac{P (R | {\vec{d}}_{j})}{P (\bar{R} | {\vec{d}}_{j})}$

Related models

There are some limitations to this framework that need to be addressed by further development:

There is no accurate estimate for the first run probabilities
Index terms are not weighted
Terms are assumed mutually independent

To address these and other concerns there are some developed models from the probabilistic relevance framework. The Binary Independence Model for one, as it is from the same author. The most known derivative of this framework is the Okapi(BM25) weighting scheme and it's BM25F brother.

References

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For Mark Shen, 36, the brand new MAS regulations have dented his upgrading plans. He lives together with his wife Annie and two daughters in a resale HDB flat. "We'd planned to buy a three-bedroom rental by taking the maximum bank mortgage and stretching it over the longest period we can. However with these new measures, the loan quantum we now qualify for can probably only get us a two-bedroom unit," says Mark. quick time period good points. The impact of the SSD is especially important as it is payable regardless whether or not the property is ultimately offered at a achieve or loss. HOUSE consumers will now must stump up much more cash upfront after the Government moved to cool the property market in an surroundings of "terribly low" rates of interest. Queenstown Condominium by Hong Leong & CDL

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+The '''probabilistic relevance model'''<ref>{{cite | author=S. E. Robertson | coauthors=K. S. Jones | title=Relevance weighting of search terms | publisher=Journal of the American Society for Information Science | pages=129–146 | date=May-June 1976 | url=http://portal.acm.org/citation.cfm?id=106783 }}</ref> was devised by Robertson and Jones as a framework for probabilistic models to come.
+It makes an estimation of the probability of finding if a document ''d<sub>j</sub>'' is relevant to a query ''q''. This model assumes that this probability of relevance depends on the query and document representations. Furthermore, it assumes that there is a portion of all documents that is preferred by the user as the answer set for query ''q''. Such an ideal answer set is called ''R'' and should maximize the overall probability of relevance to that user. The prediction is that documents in this set ''R'' are relevant to the query, while documents not present in the set are non-relevant.
+<math>sim(d_{j,q}) = \frac{P(R|\vec{d}_j)}{P(\bar{R}|\vec{d}_j)}</math>
+==Related models==
+There are some limitations to this framework that need to be addressed by further development:
+* There is no accurate estimate for the first run probabilities
+* Index terms are not weighted
+* Terms are assumed mutually independent
+To address these and other concerns there are some developed models from the probabilistic relevance framework. The [[Binary Independence Model]] for one, as it is from the same author. The most known derivative of this framework is the [[Probabilistic_relevance_model_(BM25) | Okapi(BM25)]] weighting scheme and it's BM25F brother.
+==References==
+{{reflist}}
+[[Category:Information retrieval]]
+[[Category:Probabilistic models]]

Plane of rotation: Difference between revisions

Revision as of 19:40, 6 July 2013

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References

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