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Class c c c j dan jurafsky binarized boolean feature

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Unformatted text preview: ext classifica+on domains) •  Word occurrence may maXer more than word frequency •  The occurrence of the word fantas1c tells us a lot •  The fact that it occurs 5 +mes may not tell us much more. •  Boolean Mul+nomial Naïve Bayes •  Clips all the word counts in each document at 1 25 Dan Jurafsky Boolean Mul%nomial Naïve Bayes: Learning •  From training corpus, extract Vocabulary •  Calculate P(cj) terms •  Calculate P(wk | cj) terms •  For each cj in C do docsj ← all docs with class =cj | docs j | P (c j ) ! | total # documents| Remove single doc containing •  Textj ← duplicates in each doc: all docsj •  each word w in Vocabulary •  For For each word kt ype w in docj Retain occurrences of w i o Text n•  ← # of only a single instance n f w k k j nk + ! P(wk | c j ) ! n + ! | Vocabulary | Dan Jurafsky Boolean Mul%nomial Naïve Bayes on a test document d •  First remove all duplicate words from d •  Then compute NB using the same equa+on: cNB = argmax P(c j ) c j !C 27 " i! positions P(wi | c j ) Dan Jurafsky Normal vs. Boolean Mul%nomial NB Normal Training Test Boolean Training Test 28 Doc 1 2 3 4 5 Doc 1 2 3 4 5 Words Chinese Beijing Chinese Chinese Chinese Shanghai Chinese Macao Tokyo Japan Chinese Chinese Chinese Chinese Tokyo Japan Words Chinese Beijing Chinese Shanghai Chinese Macao Tokyo Japan Chinese Chinese Tokyo Japan Class c c c j ? Class c c c j ? Dan Jurafsky Binarized (Boolean feature) Mul%nomial Naïve Bayes B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up? Sen+ment Classifica+on using Machine Learning Techniques. EMNLP ­2002, 79—86. V. Metsis, I. Androutsopoulos, G. Paliouras. 2006. Spam Filtering with Naive Bayes – Which Naive Bayes? CEAS 2006 ...
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