# DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages

arXiv: https://arxiv.org/abs/2104.08540 · HTML: https://mlsanity.com/paper/2104.08540/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 17 Apr 2021.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (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)
- [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://mlsanity.com/paper/1804.06517/) (2018)
- [RuSemShift: a dataset of historical lexical semantic change in Russian](https://mlsanity.com/paper/2010.06436/) (2020)
- [CoSimLex: A Resource for Evaluating Graded Word Similarity in Context](https://mlsanity.com/paper/1912.05320/) (2019)
- [GASC: Genre-Aware Semantic Change for Ancient Greek](https://mlsanity.com/paper/1903.05587/) (2019)
- [German in Flux: Detecting Metaphoric Change via Word Entropy](https://mlsanity.com/paper/1706.04971/) (2017)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)

## Built on by (most cited first, 26 of 26)

- [A Survey on Contextualised Semantic Shift Detection](https://mlsanity.com/paper/2304.01666/) (2023)
- [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)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [Three-part diachronic semantic change dataset for Russian](https://mlsanity.com/paper/2106.08294/) (2021)
- [LSCDiscovery: A shared task on semantic change discovery and detection in Spanish](https://mlsanity.com/paper/2205.06691/) (2022)
- [Graph-based Clustering for Detecting Semantic Change Across Time and Languages](https://mlsanity.com/paper/2402.01025/) (2024)
- [(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection](https://mlsanity.com/paper/2401.14040/) (2024)
- [Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types](https://mlsanity.com/paper/2406.03452/) (2024)
- [NorDiaChange: Diachronic Semantic Change Dataset for Norwegian](https://mlsanity.com/paper/2201.05123/) (2022)
- [RuDSI: graph-based word sense induction dataset for Russian](https://mlsanity.com/paper/2209.13750/) (2022)
- [The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change](https://mlsanity.com/paper/2311.12664/) (2023)
- [Definition generation for lexical semantic change detection](https://mlsanity.com/paper/2406.14167/) (2024)
- [The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations](https://mlsanity.com/paper/2402.14616/) (2024)
- [Towards Automating Text Annotation: A Case Study on Semantic Proximity Annotation using GPT-4](https://mlsanity.com/paper/2407.04130/) (2024)
- [SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection](https://mlsanity.com/paper/2509.11818/) (2025)
- [Contextualized Sensorimotor Norms: multi-dimensional measures of sensorimotor strength for ambiguous English words, in context](https://mlsanity.com/paper/2203.05648/) (2022)
- [The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks](https://mlsanity.com/paper/2404.00176/) (2024)
- [Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport](https://mlsanity.com/paper/2412.12569/) (2024)
- [Enriching Word Usage Graphs with Cluster Definitions](https://mlsanity.com/paper/2403.18024/) (2024)
- [Multilingual Substitution-based Word Sense Induction](https://mlsanity.com/paper/2405.11086/) (2024)
- [Historical Ink: Semantic Shift Detection for 19th Century Spanish](https://mlsanity.com/paper/2407.12852/) (2024)
- [JuniperLiu at CoMeDi Shared Task: Models as Annotators in Lexical Semantics Disagreements](https://mlsanity.com/paper/2411.12147/) (2024)
- [XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification](https://mlsanity.com/paper/2507.14578/) (2025)
- [The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage](https://mlsanity.com/paper/2606.16560/) (2026)
- [Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change](https://mlsanity.com/paper/2609.30974/) (2026)

## Abstract (arXiv)

Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. We thoroughly describe the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible - diachronic and synchronic - uses for this dataset.