1. A computer-implemented method of classifying documents, the method comprising executing instructions in a computer system to perform operations comprising: receiving a hypertext markup language (HTML) document; parsing HTML tags from the HTML document; generating a text string comprising a word for each of the HTML tags; deriving metadata from the HTML document; adding the metadata to the text string, the metadata representing a complexity value of a structure of the HTML document based upon a maximum depth of nested HTML tags in the HTML document; breaking the text string into N-grams utilizing a sliding window; utilizing a naïve Bayesian classifier trained from similar structured documents labeled as belonging to a first document class to determine a first probability that the HTML document belongs to the first document class based at least in part upon the N-grams, the naïve Bayesian classifier using the complexity value of the structure of the HTML document as a coefficient in determination of the first probability; classifying the document as belonging to the first document class based at least in part on the first probability that the HTML document belonging to the first document class that satisfies a threshold condition; and in response to the first probability not satisfying the threshold condition, utilizing the naïve Bayesian classifier trained from similar structured documents labeled as belonging to a second document class to determine a second probability that the HTML document belongs to the second document class based at least in part on the N-grams, the naïve Bayesian classifier using the complexity value of the structure of the HTML document as the coefficient in determination of the second probability.
1. A computer-implemented method of classifying documents, the method comprising executing instructions in a computer system to perform operations comprising: receiving a hypertext markup language (HTML) document; parsing HTML tags from the HTML document; generating a text string comprising a word for each of the HTML tags; deriving metadata from the HTML document; adding the metadata to the text string, the metadata representing a complexity value of a structure of the HTML document based upon a maximum depth of nested HTML tags in the HTML document; breaking the text string into N-grams utilizing a sliding window; utilizing a naïve Bayesian classifier trained from similar structured documents labeled as belonging to a first document class to determine a first probability that the HTML document belongs to the first document class based at least in part upon the N-grams, the naïve Bayesian classifier using the complexity value of the structure of the HTML document as a coefficient in determination of the first probability; classifying the document as belonging to the first document class based at least in part on the first probability that the HTML document belonging to the first document class that satisfies a threshold condition; and in response to the first probability not satisfying the threshold condition, utilizing the naïve Bayesian classifier trained from similar structured documents labeled as belonging to a second document class to determine a second probability that the HTML document belongs to the second document class based at least in part on the N-grams, the naïve Bayesian classifier using the complexity value of the structure of the HTML document as the coefficient in determination of the second probability. 2. The computer-implemented method of claim 1 , wherein the metadata added to the text string comprises an identification of an author of the HTML document to be utilized by the naïve Bayesian classifier.