This application (TwoStageRetEval.cpp) runs retrieval experiments (with/without feedback) in exactly the same way as the application RetEval.cpp, except that it always uses the two-stage smoothing method for the initial retrieval and the KL-divergence model for feedback. It thus ignores the the parameter retModel.
It recognizes all the parameters relevant to the KL-divergence retrieval model, except for the smoothing method parameter SmoothMethod which is forced to the "Two-stage Smoothing" (value of 3) and JelinekMercerLambda, which gets ignored, since it automatically estimates the value of JelinekMercerLambda using a mixture model. For details on all the parameters, see the documentation for RetEval.cpp.
To achieve the effect of the completely automatic two-stage smoothing method, the parameter DirichletPrior should be set to the estimated value of the Dirichlet prior smoothing parameter using the application EstimateDirPrior, which computes a Maximum Likelihood estimate of DirichletPrior based on "leave-one-out".
1.2.18