Towards the Computational Inference and Application of a Functional Grammar

This thesis describes a methodology for the computational learning and classification of a Systemic Functional Grammar. A machine learning algorithm is developed that allows the structure of the classifier learned to be a representation of the grammar. Within Systemic Functional Linguistics, Systemic Functional Grammar is a model of language that has explicitly probabilistic distributions and overlapping categories. Mixture modeling is the most natural way to represent this, so the algorithm developed is one of the few machine learners that extends mixture modeling to supervised learning, retaining the desirable property that it is also able to discover intrinsic unlabelled categories. As a Systemic Function Grammar includes theories of context, syntax, semantics, function and lexis, it is a particularly difficult concept to learn, and this thesis presents the first attempt to infer and apply a truly probabilistic Systemic Functional Grammar. Because of this, the machine learning algorithm is benchmarked against a collection of state-ofthe-art learners on some well-known data sets. It is shown to be comparably accurate and particularly good at discovering and exploiting attribute correlation, and in this way it can also be seen as a linearly scalable solution to the Naïve Bayes attribute independence assumption. With a focus on function at the level of form, the methodology is shown to infer an accurate functional grammar that classifies with above 90% accuracy, even across registers of text that are fundamentally very different from the one that was learned on. The discovery of unlabelled functions occurred with a high level of sophistication, and so the proposed methodology has very broad potential as an analytical and/or classification tool in a functional approach to Computational Linguistics and Natural Language Processing.