# Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types

arXiv: https://arxiv.org/abs/2406.03452 · HTML: https://mlsanity.com/paper/2406.03452/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://mlsanity.com/paper/1804.06517/) (2018)
- [Conditional Generators of Words Definitions](https://mlsanity.com/paper/1806.10090/) (2018)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021)
- [A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change](https://mlsanity.com/paper/2402.12011/) (2024)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Metaphorical Polysemy Detection: Conventional Metaphor meets Word Sense Disambiguation](https://mlsanity.com/paper/2212.08395/) (2022)

## Built on by (most cited first, 8 of 8)

- [Semantic Change Characterization with LLMs using Rhetorics](https://mlsanity.com/paper/2407.16624/) (2024)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices](https://mlsanity.com/paper/2501.09538/) (2025)
- [LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data](https://mlsanity.com/paper/2503.08042/) (2025)
- [Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives](https://mlsanity.com/paper/2506.12242/) (2025)
- [Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detection](https://mlsanity.com/paper/2604.13232/) (2026)
- [Rethinking Metrics for Lexical Semantic Change Detection](https://mlsanity.com/paper/2602.15716/) (2026)
- [Evaluating Semantic Relation Knowledge through Bidirectional Relation Prediction from Gloss Pairs](https://mlsanity.com/paper/2610.14442/) (2026)

## Abstract (arXiv)

There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalization, specialization and co-hyponymy transfer). In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings. Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types. Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.