Difference between revisions of "Extracting Features from Surnames"
imported>Ed (New page: Extracting features from Surnames entails encoding the frequency of n-grams and other features such as the string length. ==Assumption of Independence of Features== In many (actually most...) |
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| − | Extracting features from Surnames entails encoding the frequency of n-grams and other features such as the string length. | + | Extracting features from Surnames entails encoding the frequency of n-grams and other features such as the string length. Recall that 1-grams are letters or characters, 2-grams are called bigrams or digraphs, and 3-grams are called trigrams. |
==Assumption of Independence of Features== | ==Assumption of Independence of Features== | ||
Revision as of 19:57, 9 July 2009
Extracting features from Surnames entails encoding the frequency of n-grams and other features such as the string length. Recall that 1-grams are letters or characters, 2-grams are called bigrams or digraphs, and 3-grams are called trigrams.
Assumption of Independence of Features
In many (actually most) classification techniques there is an assumption of independence of features. This has two important bearings on classification using n-grams.
First many classifier '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).
