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2019 ACL ACL 2019

DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change

Abstract

AbstractLanguage 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.

🌉 Interdisciplinary Bridge — Deep Learning and Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — syntactic change
🐣 Hot Topic Early Bird — hierarchical clustering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Christin Schätzle , Hannah Booth

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Learning Types > Unsupervised Learning Interdisciplinary > Linguistics > Computational Linguistics Deep Learning > Learning Types > Representation Learning Machine Learning > Learning Paradigms > Unsupervised Learning

Keywords

text mining hierarchical clustering language change word embedding diachronic analysis syntactic change
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