3.4.1 DAmore and Mah
　　Initial information retrieval research focused on n-grams as presented in[DAmore and Mah, 1985]. The motivation behind their work was the fact thatit is difficult to develop mathematical models for terms since the potential fora term that has not been seen before is infinite. With n-grams, only a fixednumber of n-grams can exist for a given value of n. A mathematical modelwas developed to estimate the noise in indexing and to determine appropriatedocument similarity measures. DAmore and Mahs method replaces terms with n-grams in the vector spacemodel. The only remaining issue is computing the weights for each n-gram.Instead of simply using n-gram frequencies, a scaling method is used to nor-malize the length of the document. DAmore and Mahs contention was that alarge document contains more n-grams than a small document, so it should bescaled based on its length. To compute the weights for a given n-gram, DAmore and Mah estimatedthe number of occurrences of an n-gram in a document. The first simplifyingassumption was that n-grams occur with equal likelihood and follow a binomialdistribution. Hence, it was no more likely for n-gram "ABC" to occur than"DEE" The Zipfian distribution that is widely accepted for terms is not true forn-grams. DAmore and Mah noted that n-grams are not equally likely to occur,but the removal of frequently occurring terms from the document collectionresulted in n-grams that follow a more binomial distribution than the terms. DAmore and Mah computed the expected number of occurrences of an n-gram in a particular document. This is the product of the number of n-gramsin the document （the document length） and the probability that the n-gramoccurs. The n-grams probability of occurrence is computed as the ratio ofits number of occurrences to the total number of n-grams in the document.DAmore and Mah continued their application of the binomial distribution toderive an expected variance and, subsequently。