mlsanityData to 14 Oct 2026

Can Word Sense Distribution Detect Semantic Changes of Words?

arXiv 2310.10400 ↗First posted 16 Oct 2023cs.CL cs.AI cs.LGcited by 10

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Abstract (arXiv)

Semantic Change Detection (SCD) of words is an important task for various NLP applications that must make time-sensitive predictions. Some words are used over time in novel ways to express new meanings, and these new meanings establish themselves as novel senses of existing words. On the other hand, Word Sense Disambiguation (WSD) methods associate ambiguous words with sense ids, depending on the context in which they occur. Given this relationship between WSD and SCD, we explore the possibility of predicting whether a target word has its meaning changed between two corpora collected at different time steps, by comparing the distributions of senses of that word in each corpora. For this purpose, we use pretrained static sense embeddings to automatically annotate each occurrence of the target word in a corpus with a sense id. Next, we compute the distribution of sense ids of a target word in a given corpus. Finally, we use different divergence or distance measures to quantify the semantic change of the target word across the two given corpora. Our experimental results on SemEval 2020 Task 1 dataset show that word sense distributions can be accurately used to predict semantic changes of words in English, German, Swedish and Latin.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 33 of its 77 references that are in the map. First posted on arXiv 16 Oct 2023.

Builds on

33 of 77 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 1; 2026: 4.

10 papers.

  1. 2024A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Changecited by 21
  2. 2024Definition generation for lexical semantic change detectioncited by 7
  3. 2024Survey in Characterizing Semantic Changecited by 5
  4. 2024A Semantic Distance Metric Learning approach for Lexical Semantic Change Detectioncited by 5
  5. 2025SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detectioncited by 4
  6. 2024Evaluating Short-Term Temporal Fluctuations of Social Biases in Social Media Data and Masked Language Modelscited by 3
  7. 2026Transparent Semantic Change Detection with Dependency-Based Profilescited by 3
  8. 2026ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semanticscited by 2
  9. 2026Improving Interpretability of Lexical Semantic Change with Neurobiological Featurescited by 1
  10. 2026Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptationcited by 1