# Normalizing document numbers for multiclass perceptron

If I have a multiclass perceptron that I'm using for document classification, and the dataset I'm using has different numbers of documents for each class, is there any normalization that has to be done to account for this? Because right now the model seems to be overfitting to the more common classes, which I suppose makes a little sense from a logical perspective because the a priori probability would be higher anyways, but it doesn't seem like an ideal outcome.