Volume 53, Issue 3

In this article, we explore for the first time which types of individuallevel language change following major life events (MLEs) can be observed among a sample of 701 Austrian participants, and which social variables are associated with these different types of change. A cluster analysis reveals five distinct types of MLE-related language change. Regression analyses illustrates that social variables such as gender and educational attainment were significant predictors of participants’ probability of belonging to a specific cluster (i.e., a certain type of MLE-related language change). Our results further our understanding about the linguistic relevance of MLEs and, more importantly, about the different types of intra-speaker trajectories across the lifespan.

This study explores the relationship between manual text classifications linked to corpus metadata and computationally derived text groupings in corpora of Early New High German. We employ vector space models which can arrange texts in a multidimensional space based on semantic similarities and then cluster 463 texts from the ReF and GerManC corpora (1350–1800) into lexically and semantically defined groups. This operationalization of text types allows for the observation that there are more or less prototypical representatives of a text type and that there are overlaps and divergences in the development of such types. We evaluate the result of our quantitative analysis in a LASSO regression model which predicts the relative frequency of wh-relative pronouns, a linguistic variable known for genre-sensitive and historical variation. Our results show that data-driven clustering are at least complementary to traditional classifications in capturing semantic distinctions and diachronic variation in textual traditions. The findings contribute to historical text linguistics by proposing a bottom-up methodology for identifying text types and revealing how genre evolution correlates with linguistic change.