  Textual analytics based on representations of documents as bags of words have
been reasonably successful. However, analysis that requires deeper insight into
language, into author properties, or into the contexts in which documents were
created requires a richer representation. Systemic nets are one such
representation. They have not been extensively used because they required human
effort to construct. We show that systemic nets can be algorithmically inferred
from corpora, that the resulting nets are plausible, and that they can provide
practical benefits for knowledge discovery problems. This opens up a new class
of practical analysis techniques for textual analytics.
