# SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection

arXiv: https://arxiv.org/abs/2509.11818 · HTML: https://mlsanity.com/paper/2509.11818/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Evaluating the Underlying Gender Bias in Contextualized Word Embeddings](https://mlsanity.com/paper/1904.08783/) (2019)
- [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018)
- [An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels](https://mlsanity.com/paper/2203.11364/) (2022)
- [Gender-preserving Debiasing for Pre-trained Word Embeddings](https://mlsanity.com/paper/1906.00742/) (2019)
- [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014)
- [Time Masking for Temporal Language Models](https://mlsanity.com/paper/2110.06366/) (2021)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change](https://mlsanity.com/paper/1906.01688/) (2019)
- [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)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Discovering Universal Geometry in Embeddings with ICA](https://mlsanity.com/paper/2305.13175/) (2023)
- [Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change](https://mlsanity.com/paper/2210.17127/) (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)
- [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)
- [Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings](https://mlsanity.com/paper/2401.06112/) (2024)
- [A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection](https://mlsanity.com/paper/2403.00226/) (2024)
- [Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes](https://mlsanity.com/paper/2407.02820/) (2024)
- [A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications](https://mlsanity.com/paper/2406.06052/) (2024)

## Built on by (most cited first, 4 of 4)

- [DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling](https://mlsanity.com/paper/2602.11968/) (2026)
- [Rethinking Metrics for Lexical Semantic Change Detection](https://mlsanity.com/paper/2602.15716/) (2026)
- [Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance](https://mlsanity.com/paper/2605.22202/) (2026)
- [Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change](https://mlsanity.com/paper/2609.30974/) (2026)

## Abstract (arXiv)

In Semantic Change Detection (SCD), it is a common problem to obtain embeddings that are both interpretable and high-performing. However, improving interpretability often leads to a loss in the SCD performance, and vice versa. To address this problem, we propose SCDTour, a method that orders and merges interpretable axes to alleviate the performance degradation of SCD. SCDTour considers both (a) semantic similarity between axes in the embedding space, as well as (b) the degree to which each axis contributes to semantic change. Experimental results show that SCDTour preserves performance in semantic change detection while maintaining high interpretability. Moreover, agglomerating the sorted axes produces a more refined set of word senses, which achieves comparable or improved performance against the original full-dimensional embeddings in the SCD task. These findings demonstrate that SCDTour effectively balances interpretability and SCD performance, enabling meaningful interpretation of semantic shifts through a small number of refined axes. Source code is available at https://github.com/LivNLP/svp-tour .