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This study explores an approach to text generation that interprets systemic grammar as a computational representation. Terry Patten demonstrates that systemic grammar can be easily and automatically translated into current AI knowledge representations and efficiently processed by the same knowledge-based techniques currently exploited by expert systems. Thus the fundamental methodological problem of interfacing specialized computational representations with equally specialized linguistic representations can be resolved. The study provides a detailed discussion of a substantial implementation involving a relatively large systemic grammar, and a formal model of the method. It represents a fundamental and productive contribution to the literature on text generation.