text: 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.
  @cite_0: Abstract : Systemic-functional linguistics is a linguistic framework for the analysis of grammatical and semantic information in text, with a potential role in automated text mining. This report outlines essential features of the theory, its application in computational work, and the rationale for use in automated text mining, and develops a grammatical annotation scheme-- word functions--to enrich a mixed text corpus of newspaper articles and e-mails, for machine learning of semantically-oriented grammatical patterns. Testing demonstrates high accuracy in predicting word functions in unseen text in co-training with other grammatical information, providing the basis for further grammatical and semantic text processing.
summary: Kappagoda @cite_0 shows that word-function tags can be added to words using conditional random fields, in the same kind of general way that parsers add part-of-speech tags to words. These word-function tags provide hints of the systemic-functional role that words carry. This is limited because there is no hierarchy. Nevertheless, he is able to show that the process of labelling can be partially automated and that the resulting tags aid in understanding documents.