MS Access is commonly thought of as the little brother of Database engines, and not a lot of material has
been published about methods used for exploiting it during a penetration test. The aim of this paper is to
bring a lot of disparate information together into one guide.
We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.
