DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change
C. Schätzle, and H. Booth. Proceedings of the International Workshop on Computational Approaches to Historical Language Change, page 126-135. Association for Computational Linguistics, (2019)
DOI: 10.18653/v1/W19-4716
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.
%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 from:leonkokkoliadis 2019 sfbtrr161 D02
%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/sfbtrr161},
booktitle = {Proceedings of the International Workshop on Computational Approaches to Historical Language Change},
doi = {10.18653/v1/W19-4716},
interhash = {23a3d6bc1124acf314ad1349b8ebe4f7},
intrahash = {cf242fe8fdbec3cbba09ebed83df2f74},
keywords = {from:leonkokkoliadis 2019 sfbtrr161 D02},
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
}