# UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection

arXiv: https://arxiv.org/abs/2005.00050 · HTML: https://mlsanity.com/paper/2005.00050/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 30 of 32)

- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [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)
- [Contextualized language models for semantic change detection: lessons learned](https://mlsanity.com/paper/2209.00154/) (2022)
- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [Statistically significant detection of semantic shifts using contextual word embeddings](https://mlsanity.com/paper/2104.03776/) (2021)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [LSCDiscovery: A shared task on semantic change discovery and detection in Spanish](https://mlsanity.com/paper/2205.06691/) (2022)
- [Can Word Sense Distribution Detect Semantic Changes of Words?](https://mlsanity.com/paper/2310.10400/) (2023)
- [Graph-based Clustering for Detecting Semantic Change Across Time and Languages](https://mlsanity.com/paper/2402.01025/) (2024)
- [Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings](https://mlsanity.com/paper/2305.08654/) (2023)
- [Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change](https://mlsanity.com/paper/2204.05717/) (2022)
- [Analyzing Semantic Change through Lexical Replacements](https://mlsanity.com/paper/2404.18570/) (2024)
- [textitSwap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context Swapping](https://mlsanity.com/paper/2310.10397/) (2023)
- [A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection](https://mlsanity.com/paper/2403.00226/) (2024)
- [Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection](https://mlsanity.com/paper/2101.09368/) (2021)
- [A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysis](https://mlsanity.com/paper/2210.12883/) (2022)
- [Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes](https://mlsanity.com/paper/2407.02820/) (2024)
- [SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection](https://mlsanity.com/paper/2509.11818/) (2025)
- [Monitoring geometrical properties of word embeddings for detecting the emergence of new topics](https://mlsanity.com/paper/2111.03496/) (2021)
- [Predicting Long-Term Citations from Short-Term Linguistic Influence](https://mlsanity.com/paper/2210.13628/) (2022)
- [The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks](https://mlsanity.com/paper/2404.00176/) (2024)
- [To Word Senses and Beyond: Inducing Concepts with Contextualized Language Models](https://mlsanity.com/paper/2406.20054/) (2024)
- [Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport](https://mlsanity.com/paper/2412.12569/) (2024)
- [Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance](https://mlsanity.com/paper/2402.16596/) (2024)
- [Improving Interpretability of Lexical Semantic Change with Neurobiological Features](https://mlsanity.com/paper/2602.09760/) (2026)
- [Representing ELMo embeddings as two-dimensional text online](https://mlsanity.com/paper/2103.16414/) (2021)
- [Text analysis and deep learning: A network approach](https://mlsanity.com/paper/2110.04151/) (2021)
- [Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Transfer in Sense-Aware Tasks](https://mlsanity.com/paper/2505.24834/) (2025)
- [CALE : Concept-Aligned Embeddings for Both Within-Lemma and Inter-Lemma Sense Differentiation](https://mlsanity.com/paper/2508.04494/) (2025)

## Abstract (arXiv)

We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic drift over time. We analyse the performance of two contextualising architectures (BERT and ELMo) and three change detection algorithms. We find that the most effective algorithms rely on the cosine similarity between averaged token embeddings and the pairwise distances between token embeddings. They outperform strong baselines by a large margin (in the post-evaluation phase, we have the best Subtask 2 submission for SemEval-2020 Task 1), but interestingly, the choice of a particular algorithm depends on the distribution of gold scores in the test set.