mlsanityData to 7 Oct 2026

Survey in Characterizing Semantic Change

arXiv 2402.19088 ↗First posted 29 Feb 2024cs.CL cs.AIcited by 5

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Abstract (arXiv)

Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words) or semantic changes of words (new meaning to existing words). Understanding the meaning of words is vital for interpreting texts coming from different cultures (regionalism or slang), domains (e.g., technical terms), or periods. In computer science, these words are relevant to computational linguistics algorithms such as translation, information retrieval, question answering, etc. Semantic changes can potentially impact the quality of the outcomes of these algorithms. Therefore, it is important to understand and characterize these changes formally. The study of this impact is a recent problem that has attracted the attention of the computational linguistics community. Several approaches propose methods to detect semantic changes with good precision, but more effort is needed to characterize how the meaning of words changes and to reason about how to reduce the impact of semantic change. This survey provides an understandable overview of existing approaches to the textitcharacterization of semantic changes and also formally defines three classes of characterizations: if the meaning of a word becomes more general or narrow (change in dimension) if the word is used in a more pejorative or positive/ameliorated sense (change in orientation), and if there is a trend to use the word in a, for instance, metaphoric or metonymic context (change in relation). We summarized the main aspects of the selected publications in a table and discussed the needs and trends in the research activities on semantic change characterization.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 40 of its 131 references that are in the map. First posted on arXiv 29 Feb 2024.

Builds on

40 of 131 references.

References · 40

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28 more
  1. 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
  2. 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
  3. 2020SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detectioncited by 105
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  16. 2023Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysiscited by 19
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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Analyzing Semantic Change through Lexical Replacementscited by 7
  2. 2024Semantic Change Characterization with LLMs using Rhetoricscited by 4
  3. 2024A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applicationscited by 3
  4. 2025Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matricescited by 3
  5. 2025LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Datacited by 1