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.
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