%0 Conference Paper %1 schatzle-booth-2019-diahclust %A Schätzle, Christin %A Booth, Hannah %B Proceedings of the International Workshop on Computational Approaches to Historical Language Change %D 2019 %I Association for Computational Linguistics %K 2019 D02 sfbtrr161 %P 126-135 %R 10.18653/v1/W19-4716 %T DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change %U https://www.aclweb.org/anthology/W19-4716 %X 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: `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.

@inproceedings{schatzle-booth-2019-diahclust, 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: {`}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.}, added-at = {2020-03-27T12:26:55.000+0100}, author = {Sch{\"a}tzle, Christin and Booth, Hannah}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2cf242fe8fdbec3cbba09ebed83df2f74/leonkokkoliadis}, booktitle = {Proceedings of the International Workshop on Computational Approaches to Historical Language Change}, doi = {10.18653/v1/W19-4716}, interhash = {23a3d6bc1124acf314ad1349b8ebe4f7}, intrahash = {cf242fe8fdbec3cbba09ebed83df2f74}, keywords = {2019 D02 sfbtrr161}, pages = {126-135}, publisher = {Association for Computational Linguistics}, timestamp = {2020-03-27T11:26:55.000+0100}, title = {DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change}, url = {https://www.aclweb.org/anthology/W19-4716}, year = 2019 }