 The context might be a fixed-size window of previous words, so that the network predicts

from a feature vector representing the previous words.Another option is to use "future" words as well as "past" words as features, so that the estimated probability is

This is called a bag-of-words model.When the feature vectors for the words in the context are combined by a continuous operation, this model is referred to as the continuous bag-of-words architecture (CBOW).