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In this paper, an exploratory data-driven method is presented that extracts word-types from diachronic corpora that have undergone the most pronounced change in frequency of occurrence in a given period of time. Combined with statistical methods from time series analysis, the method is able to find meaningful patterns and relationships in diachronic corpora, an idea that is still uncommon in linguistics. This indicates that the approach can facilitate an improved understanding of diachronic processes.
This thesis consists of the following three papers that all have been published in international peer-reviewed journals:
Chapter 3: Koplenig, Alexander (2015c). The Impact of Lacking Metadata for the Measurement of Cultural and Linguistic Change Using the Google Ngram Data Sets—Reconstructing the Composition of the German Corpus in Times of WWII. Published in: Digital Scholarship in the Humanities. Oxford: Oxford University Press. [doi:10.1093/llc/fqv037]
Chapter 4: Koplenig, Alexander (2015b). Why the quantitative analysis of dia-chronic corpora that does not consider the temporal aspect of time-series can lead to wrong conclusions. Published in: Digital Scholarship in the Humanities. Oxford: Oxford University Press. [doi:10.1093/llc/fqv030]
Chapter 5: Koplenig, Alexander (2015a). Using the parameters of the Zipf–Mandelbrot law to measure diachronic lexical, syntactical and stylistic changes – a large-scale corpus analysis. Published in: Corpus Linguistics and Linguistic Theory. Berlin/Boston: de Gruyter. [doi:10.1515/cllt-2014-0049]
Chapter 1 introduces the topic by describing and discussing several basic concepts relevant to the statistical analysis of corpus linguistic data. Chapter 2 presents a method to analyze diachronic corpus data and a summary of the three publications. Chapters 3 to 5 each represent one of the three publications. All papers are printed in this thesis with the permission of the publishers.
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.