@inproceedings{schatzle-booth-2019-diahclust,
    title = "{D}ia{HC}lust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change",
    author = {Sch{\"a}tzle, Christin  and
      Booth, Hannah},
    editor = "Tahmasebi, Nina  and
      Borin, Lars  and
      Jatowt, Adam  and
      Xu, Yang",
    booktitle = "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-4716/",
    doi = "10.18653/v1/W19-4716",
    pages = "126--135",
    abstract = "Language change is often assessed against a set of pre-determined time periods in order to be able to trace its diachronic trajectory. This is problematic, since a pre-determined periodization might obscure significant developments and lead to false assumptions about the data. Moreover, these time periods can be based on factors which are either arbitrary or non-linguistic, e.g., dividing the corpus data into equidistant stages or taking into account language-external events. Addressing this problem, in this paper we present a data-driven approach to periodization: {\textquoteleft}DiaHClust'. DiaHClust is based on iterative hierarchical clustering and offers a multi-layered perspective on change from text-level to broader time periods. We demonstrate the usefulness of DiaHClust via a case study investigating syntactic change in Icelandic, modelling the syntactic system of the language in terms of vectors of syntactic change."
}