Grammatical Profiling for Semantic Change Detection

Andrey Kutuzov, Lidia Pivovarova, Mario Giulianelli


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.
Anthology ID:
2021.conll-1.33
Volume:
Proceedings of the 25th Conference on Computational Natural Language Learning
Month:
November
Year:
2021
Address:
Online
Venues:
CoNLL | EMNLP
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
423–434
Language:
URL:
https://aclanthology.org/2021.conll-1.33
DOI:
10.18653/v1/2021.conll-1.33
Bibkey:
Cite (ACL):
Andrey Kutuzov, Lidia Pivovarova, and Mario Giulianelli. 2021. Grammatical Profiling for Semantic Change Detection. In Proceedings of the 25th Conference on Computational Natural Language Learning, pages 423–434, Online. Association for Computational Linguistics.
Cite (Informal):
Grammatical Profiling for Semantic Change Detection (Kutuzov et al., CoNLL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.conll-1.33.pdf
Code
 glnmario/semchange-profiling