To counteract this problem, we take a hybrid approach to parameter estimation, informed by both labeled training data and the extracted distributions.
crucially, our approach, which we call ZIPF FLATTENING, has only two parameters that need to be trained using a supervised approach, and these parameters do not depend on the relation R. the approach minimizes the amount of training data we need to a practical level.