  In this paper, a method for measuring synchronic corpus (dis-)similarity put
forward by Kilgarriff (2001) is adapted and extended to identify trends and
correlated changes in diachronic text data, using the Corpus of Historical
American English (Davies 2010a) and the Google Ngram Corpora (Michel et al.
2010a). This paper shows that this fully data-driven method, which extracts
word types that have undergone the most pronounced change in frequency in a
given period of time, is computationally very cheap and that it allows
interpretations of diachronic trends that are both intuitively plausible and
motivated from the perspective of information theory. Furthermore, it
demonstrates that the method is able to identify correlated linguistic changes
and diachronic shifts that can be linked to historical events. Finally, it can
help to improve diachronic POS tagging and complement existing NLP approaches.
This indicates that the approach can facilitate an improved understanding of
diachronic processes in language change.
