The values of a vector correspond to the presence or absence of each ( lemmatized ) corpus word in the document in question ( after removing stop-words and words with very low frequency ) .4 The predictive model is a Decision Graph ( Oliver 1993 ) , which , like Snob , is based on the MML principle .
decision graphs—an extension of decision trees
J J Oliver
The input to Snob is a set of binary vectors , one vector per response document . The values of a vector correspond to the presence or absence of each ( lemmatized ) corpus word in the document in question ( after removing stop-words and words with very low frequency ) .4 The predictive model is a Decision Graph ( Oliver 1993 ) , which , like Snob , is based on the MML principle . The Decision Graph is trained on unigram and bigram lemmas in the request as input features ,5 and the identifier of the response cluster that contains the actual response for the request as the target feature .
The values of a vector correspond to the presence or absence of each ( lemmatized ) corpus word in the document in question ( after removing stop-words and words with very low frequency ) .4 The predictive model is a Decision Graph ( @@CITATION ) , which , like Snob , is based on the MML principle .