Consensus Sequence Segmentation
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.
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