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Volume 18, Issue 1
  • ISSN 1566-5852
  • E-ISSN: 1569-9854
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Abstract

Recent analyses of written text types have discovered significant frequency increases of colloquial or conversational elements, such as contractions, personal pronouns, questions or the progressive. This trend is often referred to as colloquialization. This paper presents a new perspective on colloquialization, with a special focus on the discourse marker . The paper is divided into two parts. In the first part, we present new evidence of colloquialization on the basis of the TIME Magazine Corpus ( Davies 2007 ), which allows analyses of diachronic change in recent written American English. The focus of our analysis is on highly frequent “inserts” (Biber et al. 1999: 56), which are elements such as discourse markers (e.g., and ), backchannels (, etc.), and hesitators ( and , etc.). We conclude that inserts significantly increase diachronically in TIME. In the second part of the paper, we focus on the element in its function as a discourse marker. Through a combination of quantitative and qualitative analytical steps, we analyze its diachronic development in terms of its structural contexts and its pragmatic functions, fleshing out how the process of colloquialization has affected its usage in recent written American English. We argue that the integration of corpus linguistic and pragmatic methods in this case study represents a new step towards the field of corpus pragmatics, that is, “the rapprochement between corpus linguistics and pragmatics and an integration of their key methodologies” ( Rühlemann and Aijmer 2014 : 23).

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/content/journals/10.1075/jhp.18.1.05ruh
2017-10-13
2025-04-19

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/content/journals/10.1075/jhp.18.1.05ruh
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Colloquialization in journalistic writing
Journal of Historical Pragmatics 18, 104 (2017); https://doi.org/10.1075/jhp.18.1.05ruh
/content/journals/10.1075/jhp.18.1.05ruh
/content/journals/10.1075/jhp.18.1.05ruh
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  • Article Type: Research Article
Keyword(s): colloquialization; corpus pragmatics; discourse markers; inserts; Variability-based Neighbor Clustering (VNC); well

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