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Grammatical Profiling for Semantic Change Detection

arXiv 2109.10397 ↗First posted 21 Sep 2021cs.CLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 9 of its 32 references that are in the map. First posted on arXiv 21 Sep 2021.

Builds on

9 of 32 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2018Deep contextualized word representationscited by 2,285
  4. 2018WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representationscited by 370
  5. 2020SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detectioncited by 105
  6. 2020Analysing Lexical Semantic Change with Contextualised Word Representationscited by 92
  7. 2018Diachronic word embeddings and semantic shifts: a surveycited by 90
  8. 2018Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Changecited by 35
  9. 2021Three-part diachronic semantic change dataset for Russiancited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 0; 2026: 4.

10 papers.