# RuDSI: graph-based word sense induction dataset for Russian

arXiv: https://arxiv.org/abs/2209.13750 · HTML: https://mlsanity.com/paper/2209.13750/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021)
- [Towards better substitution-based word sense induction](https://mlsanity.com/paper/1905.12598/) (2019)
- [RuSemShift: a dataset of historical lexical semantic change in Russian](https://mlsanity.com/paper/2010.06436/) (2020)
- [Three-part diachronic semantic change dataset for Russian](https://mlsanity.com/paper/2106.08294/) (2021)
- [RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language](https://mlsanity.com/paper/1803.05795/) (2018)
- [Word Sense Disambiguation for 158 Languages using Word Embeddings Only](https://mlsanity.com/paper/2003.06651/) (2020)
- [Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks](https://mlsanity.com/paper/2105.01192/) (2021)

## Built on by (most cited first, 7 of 7)

- [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)
- [MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs](https://mlsanity.com/paper/2505.19800/) (2025)
- [The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks](https://mlsanity.com/paper/2404.00176/) (2024)
- [Enriching Word Usage Graphs with Cluster Definitions](https://mlsanity.com/paper/2403.18024/) (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)

## Abstract (arXiv)

We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semi-automatic clustering of Word Usage Graphs (WUGs). Unlike prior WSI datasets for Russian, RuDSI is completely data-driven (based on texts from Russian National Corpus), with no external word senses imposed on annotators. Depending on the parameters of graph clustering, different derivative datasets can be produced from raw annotation. We report the performance that several baseline WSI methods obtain on RuDSI and discuss possibilities for improving these scores.