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Two Models

  • Model 2: Multinomial = Class conditional unigram

    • One feature Xi for each word pos in document

      • feature’s values are all words in dictionary

    • Value of Xi is the word in position i

    • Naïve Bayes assumption:

      • Given the document’s topic, word in one position in the document tells us nothing about words in other positions

    • Second assumption:

      • Word appearance does not depend on position


        P(Xi = w | c) = P(Xj = w | c) 

      • Just have one multinomial feature predicting all words

  • for all positions i,j, word w, and class c



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