Naive Bayes implementation

[Loek Cleophas]

Hi

I was looking at the naive Bayes classifier's implementation, due to  
my surprise at the n-gram parameter being used.

My understanding of 'traditional' naive Bayes is that it only  
considers probabilities related to single words/tokens, independent of  
context. Is that not what the Mahout implementation does? Are the N- 
grams used to also model N-sequences of tokens as "words" to be dealt  
with in the algorithm? Or are they used as input in some other way?

It seems it uses "N-grams" of N tokens, not N characters, from what I  
gather from NGrams.java. Or are they not related to token sequences  
but to character sequences somehow?

Any help or pointers to materials the implementation is based on would  
be appreciated. (I know that the Complementary Naive Bayes  
implementation is quite different and based on a paper introducing  
that method - but I'm wondering about the 'normal' Naive Bayes  
implementation.)

Regards,
Loek