Bayesian Classification: Nalve bayes Naive bayes is widely used in ml due to its ability to efficiently combine evidence from a wide variety of features. can be applied if the state of the world we base our classification on can be described as a series of attributes in this case, we describe the context of w in terms of the words y that occur in the context Naive bayes assumption: The attributes used for classification are conditionally independent: P(c 5y P(Zl y; in c) Ise=Ilyin P( I sg ■ Two consequences: The structure and linear ordering of words d bag of words model The presence of one word is independent of another, which is clearly untrue in text 20212/5 Natural Language Processing--Word Sense Disambiguation 92021/2/5 Natural Language Processing -- Word Sense Disambiguation 9 Bayesian Classification: Naïve Bayes ◼ Naïve Bayes: ◼ is widely used in ML due to its ability to efficiently combine evidence from a wide variety of features. ◼ can be applied if the state of the world we base our classification on can be described as a series of attributes. ◼ in this case, we describe the context of w in terms of the words vj that occur in the context. ◼ Naïve Bayes assumption: ◼ The attributes used for classification are conditionally independent: P(c|sk ) = P({vj| vj in c}|sk ) = П vj in c P(vj | sk ) ◼ Two consequences: ◼ The structure and linear ordering of words is ignored: bag of words model. ◼ The presence of one word is independent of another, which is clearly untrue in text