Reassessing Language Change: The Challenge of Real Time

One of the major challenges in linguistic research is unravelling the process of language change. Sociolinguists have made great strides by analysing change in apparent time, comparing the language use of successive generations at a given point in time. Information on real-time change is harder to come by but, thanks to the digital turn in the humanities, more data is available that enables the diachronic approach. However, empirical work on change over time is still fragmented and provides only a patchy coverage of certain aspects of change.

In this project we aim to bring together a large body of work on real-time language change, and by doing so, to make empirical research more cumulative. We will achieve this by compiling a Language Change Database (LCD) to serve as the basis for further work ranging from statistical modelling and systematic reviews to replication with other data sets, and studies of sociolinguistic typologies.

Under the aegis of the VARIENG Research Unit, this open-access database will serve as the basis for further work ranging from statistical modelling and systematic reviews to replication with other data sets and studies of sociolinguistic typologies. We will address key sociolinguistic hypotheses on the rate and direction of change, register and, in collaboration with Prof. Peter Trudgill, change and community type.

We will also do original research in domains that have hitherto largely fallen outside the empirical linguist’s remit, i.e., lexical semantic change and derivational vs. inflectional change. We expect that this work will significantly impact on our understanding of how language change unfolds in real time. The results apply to English and other languages with similar resources such as Dutch and German.

Besides conducting qualitative and quantitative meta-analyses based on LCD, we will develop innovative methods for studying register change at the level of parts of speech, as well as for analysing diachronic processes that are not easily categorized as linguistic variables. Building on our continuing collaboration with computer scientists, we will apply existing methods to new kinds of changes, develop them further by adding additional measures and make them more user-friendly and thus more accessible to corpus linguists.

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