When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word ''hello'' would be represented as {''he'': 1, ''el'': 1, ''ll'': 1, ''lo'': 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as ''th'' in English or ''ch'' in German .
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