One typical strategy for spam detection is to convert text to a "bag of words" representation[0]. If you take this bag of words representation and hash all the values, then rather than training words like ED, you are training on word number 19213123. The number of these hashed values is smaller than the number of words, just like a hash table, and this generally doesn't harm the accuracy of the machine learning. When you receive feedback on the classification (from people reporting spam or people marking things as not spam), you just turn the reported email into a bag of hashed words and feed that change into your model.

Because the order of the entries in the bag of words is arbitrary, and the words have been hashed, it is impossible to go back from a bag of words representation to the original email. I don't know if this is what google does, but it is pretty normal to do so.

0: https://en.wikipedia.org/wiki/Bag-of-words_model