Revisiting Inference. A Multidisciplinary Approach

Doctoral Thesis / Dissertation from the year 2016 in the subject Speech Science / Linguistics, University of Sfax, language: English, abstract: Previous literature on inference has often approached it from a single perspective offering but an incomplete understanding of its full potential. For this reason, this study proposes a multidisciplinary approach to inference. Synthesizing the contributions of Logic and Pragmatics, this research builds a new Inference Taxonomy. The study claims that this newly developed theoretical model can be transformed into a discourse descriptive tool meant to complement traditional tools offered by Discourse Analysis. The most important contributions of the research is that it attempts to offer a new understanding of inference, develop that theoretical understanding into a new instrument that serves to describe discourse, and gauge the functioning of that instrument. In order to test the functioning of the new descriptive tool and to explore the insights it can offer, the study makes use of a corpus-based approach. It applies theoretical and methodological findings to a corpus of ten texts. The analysis consists in providing a systematic description of the different types of inference underlying the texts, which is congruent with the inherently qualitative method used. Quantification is also used with the purpose of investigating the implications of the qualitative analysis. The corpus analysis illustrates how the new inference-based instrument can provide detailed descriptions of the inferential structure of texts. It equally sheds light on the correlations that may exist between inferences found in a text and the type or topic of this text, possible similarities between different texts in inferential terms, clear tendencies or common inferential features, et cetera. Thus, the findings substantiate the research major claims. The outcome of the study is a fine-tuned multidisciplinary inference taxonomy that is translatable into an in