Text Classification and Naïve Bayes Formalizing the Naïve Bayes Classifier.

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Presentation transcript:

Text Classification and Naïve Bayes Formalizing the Naïve Bayes Classifier

Dan Jurafsky Bayes’ Rule Applied to Documents and Classes For a document d and a class c

Dan Jurafsky Naïve Bayes Classifier (I) MAP is “maximum a posteriori” = most likely class Bayes Rule Dropping the denominator

Dan Jurafsky Naïve Bayes Classifier (II) Document d represented as features x1..xn

Dan Jurafsky Naïve Bayes Classifier (IV) How often does this class occur? O(|X| n |C|) parameters We can just count the relative frequencies in a corpus Could only be estimated if a very, very large number of training examples was available.

Dan Jurafsky Multinomial Naïve Bayes Independence Assumptions Bag of Words assumption: Assume position doesn’t matter Conditional Independence: Assume the feature probabilities P(x i |c j ) are independent given the class c.

Dan Jurafsky Multinomial Naïve Bayes Classifier

Dan Jurafsky Applying Multinomial Naive Bayes Classifiers to Text Classification positions  all word positions in test document

Text Classification and Naïve Bayes Formalizing the Naïve Bayes Classifier