  In this paper we introduce a method to detect words or phrases in a given
sequence of alphabets without knowing the lexicon. Our linear time unsupervised
algorithm relies entirely on statistical relationships among alphabets in the
input sequence to detect location of word boundaries. We compare our algorithm
to previous approaches from unsupervised sequence segmentation literature and
provide superior segmentation over number of benchmarks.
