Beyond Bags of Words: Inferring Systemic Nets

TL;DR

This paper introduces a method to algorithmically infer systemic nets from text corpora, enabling richer linguistic analysis and practical knowledge discovery without extensive manual effort.

Contribution

It presents a novel approach to automatically infer systemic nets, expanding the tools available for textual analytics beyond traditional bag-of-words models.

Findings

Systemic nets can be inferred algorithmically from corpora.

Inferred nets are plausible and meaningful.

Practical benefits for knowledge discovery are demonstrated.

Abstract

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.