A Probabilistic Representation of Systemic Functional Grammar

The notion of language as probabilistic is well known within Systemic Functional Linguistics. Aspects of language are discussed as meaningful tendencies, not as deterministic rules. In past computational representations of functional grammars, this probabilistic property has typically been omitted. This paper will present the results of a recent project aimed at the computational learning, representation and application of a fundamentally probabilistic functional grammar. Recent advances in machine learning have allowed the large scale inference of truly probabilistic representations of language for the first time. In this work, a machine learning algorithm is developed that learns aspects of a functional grammar from labeled text. This is represented probabilistically, in the sense that there is a measurable gradation of functional realisation between all categories. Looking at a single term, this allows that term to be described as realising multiple functions simultaneously. Looking at all the terms in a text or register, this allows us to examine the relationships between the functions with respect to the closeness and/or overlap of functions, and the extent to which these relationships differ between different texts or registers. With a focus on function within the noun phrase (nominal group), the methodology is shown to infer an accurate description of functional categories that classifies new examples with above 90% accuracy, even across registers of text that are very different from the text that was learned on. Importantly, the learner is deliberately restricted from remembering specific words, so that the functions are (necessarily) learned and represented in terms of features such as part-of-speech, context and collocational tendencies. This restriction allows the successful application to different registers and demonstrates that function is much more a product of context than a property of the words themselves. The inferred grammar is also shown to have interesting applications in the analysis of layers of delicacy. The discovery of finer delicacies occurs with a high level of sophistication, indicating a potential for the automated discovery and representation of lexis as most delicate grammar.