1. A method comprising: training an email system for determining spam, where training includes at least the following: tokenizing at least a portion of a first email message to create a first token; creating a second token comprising a phonetic equivalent of the first token, wherein creating the phonetic equivalent of the first token comprises: identifying a string of characters within the first token, the string of characters including a non-alphabetic character; and removing the non-alphabetic character from the string of characters; determining, from each token created, a spam probability for the first email message; determining whether each token created is present in a database of tokens; in response to a determination that a token created is not present in the database of tokens, assigning a probability value for the token created as spam and adding the token created and the probability value to the database of tokens; and in response to a determination that the token created is present in the database of tokens, updating an assigned probability value for the token present to reflect contribution of the token created; and filtering a second email message according to the training.
1. A method comprising: training an email system for determining spam, where training includes at least the following: tokenizing at least a portion of a first email message to create a first token; creating a second token comprising a phonetic equivalent of the first token, wherein creating the phonetic equivalent of the first token comprises: identifying a string of characters within the first token, the string of characters including a non-alphabetic character; and removing the non-alphabetic character from the string of characters; determining, from each token created, a spam probability for the first email message; determining whether each token created is present in a database of tokens; in response to a determination that a token created is not present in the database of tokens, assigning a probability value for the token created as spam and adding the token created and the probability value to the database of tokens; and in response to a determination that the token created is present in the database of tokens, updating an assigned probability value for the token present to reflect contribution of the token created; and filtering a second email message according to the training. 4. The method of claim 1 , wherein determining the spam probability comprises: assigning a spam probability value to the token; and generating a Bayesian probability value using the spam probability value assigned to the token.