Text categorization is the task of assigning a given document to one of a fixed set of categories on the basis of the text it contains. Naive Bayes models are often used for this task. In these models, the query variable is the document category, and the “effect” variables are the presence or absence of each word in the language; the assumption is that words occur independently in documents, with frequencies determined by the document category.
Explain precisely how such a model can be constructed, given as “training data” a set of documents that have been assigned to categories.
Explain precisely how to categorize a new document.
Is the conditional independence assumption reasonable? Discuss.