This paper uses diachronic corpus data to visualize language change in a dynamic fashion. Bivariate and multivariate data sets form the input for so-called motion charts, i.e. series of diachronically ordered scatterplots that can be viewed in sequence. Based on data from COHA (Davies 2010), two case studies illustrate recent changes in American English. The first study visualizes change in a diachronic analysis of ambicategorical nouns and verbs such as hope or drink; the second study shows structural change in the behavior of complement-taking predicates such as expect or remember. Whereas motion charts are typically used to represent bivariate data sets, it is argued here that they are also useful for the analysis of multivariate data over time. The present paper submits multivariate diachronic data to a multi-dimensional scaling analysis. Viewing the resulting data points in separate time slices offers a holistic and intuitive representation of complex linguistic change.
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