# Definition generation for lexical semantic change detection

arXiv: https://arxiv.org/abs/2406.14167 · HTML: https://mlsanity.com/paper/2406.14167/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Conditional Generators of Words Definitions](https://mlsanity.com/paper/1806.10090/) (2018)
- [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)
- [Contextualized language models for semantic change detection: lessons learned](https://mlsanity.com/paper/2209.00154/) (2022)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Semeval-2022 Task 1: CODWOE -- Comparing Dictionaries and Word Embeddings](https://mlsanity.com/paper/2205.13858/) (2022)
- [Three-part diachronic semantic change dataset for Russian](https://mlsanity.com/paper/2106.08294/) (2021)
- [Can Word Sense Distribution Detect Semantic Changes of Words?](https://mlsanity.com/paper/2310.10400/) (2023)
- [Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change](https://mlsanity.com/paper/2204.05717/) (2022)

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

- [Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes](https://mlsanity.com/paper/2407.02820/) (2024)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [Improving Interpretability of Lexical Semantic Change with Neurobiological Features](https://mlsanity.com/paper/2602.09760/) (2026)
- [DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling](https://mlsanity.com/paper/2602.11968/) (2026)
- [Do I look like a `cat.n.01` to you? A Taxonomy Image Generation Benchmark](https://mlsanity.com/paper/2503.10357/) (2025)
- [Explaining novel senses using definition generation with open language models](https://mlsanity.com/paper/2509.26181/) (2025)
- [Rethinking Metrics for Lexical Semantic Change Detection](https://mlsanity.com/paper/2602.15716/) (2026)

## Abstract (arXiv)

We use contextualized word definitions generated by large language models as semantic representations in the task of diachronic lexical semantic change detection (LSCD). In short, generated definitions are used as `senses', and the change score of a target word is retrieved by comparing their distributions in two time periods under comparison. On the material of five datasets and three languages, we show that generated definitions are indeed specific and general enough to convey a signal sufficient to rank sets of words by the degree of their semantic change over time. Our approach is on par with or outperforms prior non-supervised sense-based LSCD methods. At the same time, it preserves interpretability and allows to inspect the reasons behind a specific shift in terms of discrete definitions-as-senses. This is another step in the direction of explainable semantic change modeling.