TY - RPRT U1 - Arbeitspapier A1 - Koplenig, Alexander T1 - A fully data-driven method to identify (correlated) changes in diachronic corpora N2 - 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. KW - diachronic corpora KW - time series analysis KW - text mining KW - COHA KW - BNC KW - Google Books Ngram corpora KW - Englisch KW - Text Mining KW - Wortschatz KW - Sprachgeschichte KW - Korpus Y1 - 2015 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-42569 U6 - https://doi.org/10.1080/09296174.2017.1311447 DO - https://doi.org/10.1080/09296174.2017.1311447 N1 - Der Artikel wurde vom "Journal of Quantitative Linguistics" zur Veröffentlichung angenommen und ist am 26. April 2017 online erschienen: http://dx.doi.org/10.1080/09296174.2017.1311447. This is a preprint of an accepted article published by Taylor & Francis in "Journal of quantitative linguistics" on 26 April 2017, available online: http://dx.doi.org/10.1080/09296174.2017.1311447. SP - 46 S1 - 46 CY - Mannheim ET - Version 1, 08-2015 ER -