A Simple Method for Estimating Term Mutual Information

The ability to formally analyze and automatically measure statistical dependence of terms is a core problem in many areas of science. One of the commonly used tools for this is the expected mutual information (MI) measure. However, it seems
that MI methods have not achieved their potential. The main problem in using MI of terms is to obtain actual probability distributions estimated from training data, as the true distributions are invariably not known. This study focuses on the problem and proposes a novel but simple method for estimating probability distributions. Estimation functions are introduced; mathematical meaning of the functions is interpreted and the verification conditions are discussed. Examples are provided to illustrate the possibility of failure of applying the method if the verification conditions are not satisfied. An extension of the method is considered.