We explored various ways to combine the results from the four classifiers.
for the first part of the experimental setup, we consider the following classification framework: given the (probabilistic) output , the classifier combination problem can be viewed a probability interpolation problem -compute the class probability distribution conditioned on the joint classifier output: where # is the i th classifier's output, is an observable context (e.g., a word trigram) and each of the classifiers votes with equal weight for the class that is most likely under its model, and the class receiving the largest number of votes wins (i.e., it is selected as the classification output).