two learning schemes in information retrieval
Two methods are given to improve weighting schemes by using relevance information of a set of queries. The first method is to estimate parameter values of two independence models in information retrieval  the binary independence model and the non-binary independence model. The parameters estimated here are used to calculate optimal weights for terms in a different set of queries. Performance of this estimation is compared to the inverse document frequency method, the cosine measure, and the statistical similarity measure. The second method is to learn optimal weights of the non-binary independence model adaptively by a learning formula. Experiments are performed on three different document collections CISI, MEDLARS, and CRN4NUL for both methods, and results are reported. Both methods show improvements compared to the existing weighting schemes. Experimental results show that the second method gives slightly better performance than the first one, and has simpler implementation.
learning scheme informal information retrieval method relevance queries estimates values model optimality performance documentation measurement statistics similarity measure experience collect experimentation implementation