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The statistical methods derived and described in this thesis provide new waysto elucidate the structural properties of text and other symbolic sequences.Generically, these methods allow detection of a difference in the frequency ofa single feature, the detection of a difference between the frequencies of anensemble of features and the attribution of the source of a text. These threeabstract tasks suffice to solve problems in a wide variety of settings.Furthermore, the techniques described in this thesis can be extended to providea wide range of additional tests beyond the ones described here.  A variety of applications for these methods are examined in detail. Theseapplications are drawn from the area of text analysis and genetic sequenceanalysis. The textually oriented tasks include finding interesting collocationsand cooccurent phrases, language identification, and information retrieval. Thebiologically oriented tasks include species identification and the discovery ofpreviously unreported long range structure in genes. In the applicationsreported here where direct comparison is possible, the performance of these newmethods substantially exceeds the state of the art.  Overall, the methods described here provide new and effective ways to analysetext and other symbolic sequences. Their particular strength is that they dealwell with situations where relatively little data are available. Since thesemethods are abstract in nature, they can be applied in novel situations withrelative ease.

Finding Structure in Text, Genome and Other Symbolic Sequences