"Text, a sequence of words, is not a byproduct of a statistical process, it is a byproduct of a cognitive process."

That cognitive inference process is what we've formalised as probability theory.

Whenever you do /anything/ your brain may be selecting from a probability distribution over things that can be done immediately.

As for text as continuous data just chuck it in glove, word2vec, lexvec or fasttext. Given enough training you could model the velocity of concepts as they're being introduced to the dataset / model.

Also, on the whole, shallowish learning can be applied to Natural Languages pretty easily. Keras includes a memory network (LSTM with an autoencoder) that averages around 98% accuracy on the bAbI 10k Q/A task.