NLP 2021 @ BGU.CS
Lecture #08 / Quiz #07
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When performing feature extraction over words, list 3 types of features that are typically useful. For each one, provide an estimate of the expected number of values for the feature given a training dataset containing N tokens and V distinct words.
List 3 methods that are useful to reduce the dimension of a Bag of Word feature representation for documents. For each method, explain why the simplification induced by the method is intuitively justified.
Learning a Bayes classifier without independence assumptions requires an unrealistic number of training examples. Compute the number of parameters to be learned for a model Y = f(X) where Y is a boolean variable, and X is vector of N boolean features. What is the number of parameters under Naive Bayes assumptions?  p(Y | X) = p(X | Y) p(Y) / p(X) - (1) What is the Number of parameters for p(Y) - (2) What is the Number of parameters for p(X | Y) - (3) What is the Number of parameters under Naive Bayes independence assumption.
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