Corpus Linguistics and Conversation Analysis at the Interface: Theoretical Perspectives, Practical Outcomes

Abstract

AbstractThis chapter offers, in the first instance, a theoretical perspective on the merits and potential problems associated with a combined Corpus Linguistics (CL) and Conversation Analytic (CA) (henceforth, CLCA) approach to the study of language. Secondly, the chapter considers some of the practical outcomes offered by a combined CLCA approach and looks at how this methodology might be operationalized using spoken corpora.When seen from epistemological and ontological perspectives, CL and CA have such different origins and research foci that some researchers might almost say they are incompatible. CL offers insights into the overall landscape of a corpus by focusing on specific features of the data such as word frequency, concordances, multi-word units and keyness. The analysis is highly quantitative, uses a large sample of data and sets out to describe patterns and key linguistic features. CA, on the other hand, looks at talk- in-interaction, focusing on turn-taking and turn sequencing in order to uncover how social actions are shared and how interactants achieve intersubjectivity or mutual understanding. Using a detailed, microscopic approach to spoken data, CA sets out to explain how interactants co-construct meanings, repair breakdowns and orient to each other. The analysis is more qualitative, though the procedures used are precise.In this study, I set out the various arguments for and against combining CL with CA from both theoretical and practical perspectives. While there are certainly issues associated with a CLCA methodology, I will argue that the benefits of this approach to language study outweigh the shortcomings. From a more practical perspective, the chapter suggests ways in which a CLCA approach has the potential to offer new insights into spoken texts by considering how linguistic and interactional features interface in the co-construction of meaning in an educational context.KeywordsLinguistic FeatureAdjacency PairConversation AnalysisSmall Group TeachingDiscourse MarkerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.