Changes

Extracting features from Surnames surnames entails encoding the frequency of [http://en.wikipedia.org/wiki/Ngram n-grams] and other features such as the string length. Recall that 1-grams are letters or characters, also called unigrams, 2-grams are called bigrams or digraphs, and 3-grams are called trigrams. In some applications entire words, sentences or other tokens are used as grams.

First many classifier classifiers 'require' a feature matrix of full column rank, so including a variable like the length of the name along with the n-gram frequencies introduces a linear dependence between the columns. Thus ; coding EGAN as having length 4 along with the 1-grams E, G, A, and N, clearly introduces no new information. The same is true for bigrams EG, GA, and AN, or trigrams EGA and GAN, and so forth. Likewise coding both bigrams and trigrams introduces no new information.

Second the assumption of independence among features means that with an n-gram encoding the sequence information is lost. That is EGA and GAN are assumed to be uncorrelated, though clearly they are not (as they overlap by GA). Thus there is a potential for improvement by including positional features. One way of denoting the start and end of the string is to add a space to the gram set and delimit surname with spaces. Thus EGAN would be coded in trigrams as " EG", "EGA", "GAN", and "AN ". As space characters can be difficult to spot, a hash (#) or underscore (_) is often used in its place.