Towards estimating global probabilities of evaluation in English based on automatic extraction of least delicate Appraisal in large corpora

Abstract

Systemic Functional Linguistics (hereafter SFL) hypothesizes that language systems globally follow two probability distributions (equiprobable or maximally skewed). Accordingly, a particular register, corpus or text is defined probabilistically as a local resetting of the global probabilities; or in other words, a statistical deviation of the local probabilities from the two global distributions of language. However, the original context in which this hypothesis has been proposed and, later, tested is the lexico-grammar. The current study addresses this hypothesis, both computationally and statistically, with respect to the discourse semantics system of appraisal. Through the implementation of automatic classification of least delicate engagement and attitude polarity in fifteen English corpora, this paper approaches three questions concerning the global patterns of appraisal choices in English, the intrastratal conditioning between the two appraisal subsystems, and the local, register-specific deviations of local probabilities. The computationally-based statistical analysis indicates that, globally, least delicate engagement and attitudepolarity are equiprobable, whereas, within the polarity subsystem itself, choices are skewed in favor of the positive. The findings of this paper also show how local deviations of appraisal probabilities in the corpora cluster the relevant registers topologically in terms of what is contextually expected and what is contextually divergent. A major implication of this paper is that computational methods to large-scale corpora can substantiate the theoretical premises of SFL.