y most classifiers) that work on all profiles.

An interesting solution to that problem is to rely on mutual information to order the input space[2], and select the top N n-grams or full-words that have the greatest mutual-information[3] with the gender random variable. Intuitively, the mutual information between two random variables measures how much knowing one of these variables reduces uncertainty about the other. Here's how we computed the mutual information in our case:

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