Formulating an explicit probabilistic model permits us to cleanly separate assumptions about the input and properties of likely segmentations from details of algorithms used to find such solutions.
this paper demonstrates the importance of contextual dependencies for word segmentation by comparing two probabilistic models that differ only in that the first assumes that the probability of a word is independent of its local context, while the second incorporates bigram dependencies between adjacent words.