Overview An n-gram -- in the context of parsing natural languages such as English -- is a sequence of n consecutive tokens (which we might define as characters separated by whitespace) from some passage of text. Based on the following passage: I really really like cake. We have the following 2-grams: [('I', 'really'), ('really', 'really'), ('really', 'like'), ('like', 'cake.')] And the following 3-grams: [('I', 'really', 'really'), ('really', 'really', 'like'), ('really', 'like', 'cake.')] (I omit a 1-gram listing because it would merely be a list of all tokens in the original text.) Among other things, n-grams are useful for describing the vocabulary of and statistical correlation between tokens in a sample body of text (e. g., as taken from a book). We can use an n-gram model to determine the likelihood of finding a particular sequence of words after another. This information, in turn, can be used to generate passages of text that statistically mimic the sample. We can convert the above 3-gram list into the following lookup structure (i. e., a dictionary mapping strings to lists of 2-tuples), where the first token of each n-gram maps to all sequences that follow it in the text: