A comparison of criteria for maximum entropy/minimum divergence feature selection

In this paper we study the gain, a naturally-arising statistic from the theory of MEMD modeling [2], as a figure of merit for selecting features for an MBMD language model. We compare the gain with two popular alternatives-empirical activation and mutual information-and argue that the gain is the preferred statistic, on the grounds that it directly measures a feature's contribution to improving upon the base model.