Minimum entropy approach to word segmentation problems

Given a sequence composed of a limited number of characters, we try to “read” it as a “text”. This involves segmenting the sequence into “words”. The difficulty is to distinguish good segmentation from enormous numbers of random ones. Aiming at revealing the nonrandomness of the sequence as strongly as possible, by applying maximum likelihood method, we find a quantity called segmentation entropy that can be used to fulfill the aim. Contrary to commonplace where maximum entropy principle was applied to obtain good solution, we chose to minimize the segmentation entropy to obtain good segmentation. The concept developed in this letter can be used to study the noncoding DNA sequences, e.g., for regulatory elements prediction, in eukaryote genomes.