Grammatical Profiling for Semantic Change Detection

Open Access
Authors
Publication date 2021
Host editors
  • A. Bisazza
  • O. Abend
Book title The 25th Conference on Computational Natural Language Learning
Book subtitle CoNLL 2021 : proceedings of the conference : November 10-11, 2021, online
ISBN (electronic)
  • 9781955917056
Event 25th Conference on Computational Natural Language Learning
Pages (from-to) 423–434
Publisher Stroudsburg, PA: The Association for Computational Linguistics
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
Abstract
Semantics, morphology and syntax are strongly interdependent. However, the majority of computational methods for semantic change detection use distributional word representations which encode mostly semantics. We investigate an alternative method, grammatical profiling, based entirely on changes in the morphosyntactic behaviour of words. We demonstrate that it can be used for semantic change detection and even outperforms some distributional semantic methods. We present an in-depth qualitative and quantitative analysis of the predictions made by our grammatical profiling system, showing that they are plausible and interpretable.
Document type Conference contribution
Note With supplementary video
Language English
Published at https://doi.org/10.18653/v1/2021.conll-1.33
Downloads
2021.conll-1.33 (Final published version)
Supplementary materials
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