# Graph-based Clustering for Detecting Semantic Change Across Time and Languages

arXiv: https://arxiv.org/abs/2402.01025 · HTML: https://mlsanity.com/paper/2402.01025/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 60 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings](https://mlsanity.com/paper/1805.06297/) (2018)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection](https://mlsanity.com/paper/2005.00050/) (2020)
- [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021)
- [Capturing Evolution in Word Usage: Just Add More Clusters?](https://mlsanity.com/paper/2001.06629/) (2020)
- [Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora](https://mlsanity.com/paper/2112.14330/) (2021)
- [Contextualized language models for semantic change detection: lessons learned](https://mlsanity.com/paper/2209.00154/) (2022)
- [On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models](https://mlsanity.com/paper/1704.02497/) (2017)
- [CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations](https://mlsanity.com/paper/2005.06602/) (2020)
- [Substitution-based Semantic Change Detection using Contextual Embeddings](https://mlsanity.com/paper/2309.02403/) (2023)
- [A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT](https://mlsanity.com/paper/2004.09205/) (2020)
- [UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection](https://mlsanity.com/paper/2012.00004/) (2020)
- [SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces](https://mlsanity.com/paper/2010.00857/) (2020)
- [False-Friend Detection and Entity Matching via Unsupervised Transliteration](https://mlsanity.com/paper/1611.06722/) (2016)
- [Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection](https://mlsanity.com/paper/2101.09368/) (2021)
- [Cross-lingual Word Analogies using Linear Transformations between Semantic Spaces](https://mlsanity.com/paper/1807.04175/) (2018)
- [A Computational Approach to Measuring the Semantic Divergence of Cognates](https://mlsanity.com/paper/2012.01288/) (2020)

## Built on by (most cited first, 10 of 10)

- [SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection](https://mlsanity.com/paper/2509.11818/) (2025)
- [Syntactic Language Change in English and German: Metrics, Parsers, and Convergences](https://mlsanity.com/paper/2402.11549/) (2024)
- [Presence or Absence: Are Unknown Word Usages in Dictionaries?](https://mlsanity.com/paper/2406.00656/) (2024)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking](https://mlsanity.com/paper/2601.22410/) (2026)
- [Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detection](https://mlsanity.com/paper/2604.13232/) (2026)
- [Exploring Diachronic and Diatopic Changes in Dialect Continua: Tasks, Datasets and Challenges](https://mlsanity.com/paper/2407.04010/) (2024)
- [Exploring Sound Change Over Time: A Review of Computational and Human Perception](https://mlsanity.com/paper/2407.05092/) (2024)
- [Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling](https://mlsanity.com/paper/2408.05184/) (2024)
- [Between Century and Poet: Graph-Based Lexical Semantic Change in Persian Poetry](https://mlsanity.com/paper/2604.06674/) (2026)

## Abstract (arXiv)

Despite the predominance of contextualized embeddings in NLP, approaches to detect semantic change relying on these embeddings and clustering methods underperform simpler counterparts based on static word embeddings. This stems from the poor quality of the clustering methods to produce sense clusters -- which struggle to capture word senses, especially those with low frequency. This issue hinders the next step in examining how changes in word senses in one language influence another. To address this issue, we propose a graph-based clustering approach to capture nuanced changes in both high- and low-frequency word senses across time and languages, including the acquisition and loss of these senses over time. Our experimental results show that our approach substantially surpasses previous approaches in the SemEval2020 binary classification task across four languages. Moreover, we showcase the ability of our approach as a versatile visualization tool to detect semantic changes in both intra-language and inter-language setups. We make our code and data publicly available.