12. A computer-implemented method for creating a disfluence identifier model comprising: analyzing the distribution of speech repairs in transcribed speech in a processor-based natural language processing system executing program code; designating major categories of tags in a multi-level hierarchy of tags in a part-of-speech tagger unit of the natural language processing system, wherein the major categories comprise a noun category, a verb category, an adjective category, and an adverb category; choosing conditioning variables commensurate with the distribution of speech repairs; using a rough copy identifier with the conditioning variables to generate a feature set, in a disfluence identifier unit of the natural language processing system, wherein the rough copy identifier allows single mismatches in POS-tag sequences of rough copy, and wherein POS-tags are subordinate categories to the designated major categories in the multi-level hierarchy; and, weighting the feature set according to an iterative algorithm run on training data and executed in the natural language processing system, wherein the rough copy identifier allows single mismatches in POS-tag sequences of rough copy.
12. A computer-implemented method for creating a disfluence identifier model comprising: analyzing the distribution of speech repairs in transcribed speech in a processor-based natural language processing system executing program code; designating major categories of tags in a multi-level hierarchy of tags in a part-of-speech tagger unit of the natural language processing system, wherein the major categories comprise a noun category, a verb category, an adjective category, and an adverb category; choosing conditioning variables commensurate with the distribution of speech repairs; using a rough copy identifier with the conditioning variables to generate a feature set, in a disfluence identifier unit of the natural language processing system, wherein the rough copy identifier allows single mismatches in POS-tag sequences of rough copy, and wherein POS-tags are subordinate categories to the designated major categories in the multi-level hierarchy; and, weighting the feature set according to an iterative algorithm run on training data and executed in the natural language processing system, wherein the rough copy identifier allows single mismatches in POS-tag sequences of rough copy. 14. A method as in claim 12 wherein the rough copy identifier matches only the highest level POS tags in the multi-level hierarchy.