When working with textual data, context can be very important. As we discussed before, we sometimes lose this context in vector representations, knowing only the count of each word. N-grams, and in particular, bi-grams are going to help us solve this problem, at least to some extent.
An n-gram is a contiguous sequence of n items in the text. In our case, we will be dealing with words being the item, but depending on the use case, it could be even letters, syllables, or sometimes in the case of speech, phonemes. A bi-gram is when n = 2.
One way bi-grams are calculated in the text is by calculating the conditional probability of a token given by the preceding token. It can also just be calculated by choosing words that appear next to each other, but it is more useful for us to use bi-grams that are more likely to appear as a pair. Such a bi-gram...
