# Improving Interpretability of Lexical Semantic Change with Neurobiological Features

arXiv: https://arxiv.org/abs/2602.09760 · HTML: https://mlsanity.com/paper/2602.09760/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 68 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [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)
- [Analysing Lexical Semantic Change with Contextualised Word Representations](https://mlsanity.com/paper/2004.14118/) (2020)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018)
- [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014)
- [Dynamic Word Embeddings](https://mlsanity.com/paper/1702.08359/) (2017)
- [XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization](https://mlsanity.com/paper/2010.06478/) (2020)
- [Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift](https://mlsanity.com/paper/1912.01072/) (2019)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection](https://mlsanity.com/paper/2005.00050/) (2020)
- [A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains](https://mlsanity.com/paper/1906.02979/) (2019)
- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Short-Term Meaning Shift: A Distributional Exploration](https://mlsanity.com/paper/1809.03169/) (2018)
- [Capturing Evolution in Word Usage: Just Add More Clusters?](https://mlsanity.com/paper/2001.06629/) (2020)
- [Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora](https://mlsanity.com/paper/2112.14330/) (2021)
- [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023)
- [Statistically significant detection of semantic shifts using contextual word embeddings](https://mlsanity.com/paper/2104.03776/) (2021)
- [AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples](https://mlsanity.com/paper/2104.08639/) (2021)
- [Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings](https://mlsanity.com/paper/2012.15353/) (2020)
- [Can Word Sense Distribution Detect Semantic Changes of Words?](https://mlsanity.com/paper/2310.10400/) (2023)
- [Substitution-based Semantic Change Detection using Contextual Embeddings](https://mlsanity.com/paper/2309.02403/) (2023)
- [Definition generation for lexical semantic change detection](https://mlsanity.com/paper/2406.14167/) (2024)
- [Imparting Interpretability to Word Embeddings while Preserving Semantic Structure](https://mlsanity.com/paper/1807.07279/) (2018)
- [Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings](https://mlsanity.com/paper/2310.10262/) (2023)
- [textitSwap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context Swapping](https://mlsanity.com/paper/2310.10397/) (2023)

## Built on by (most cited first, 1 of 1)

- [Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detection](https://mlsanity.com/paper/2604.13232/) (2026)

## Abstract (arXiv)

Lexical Semantic Change (LSC) is the phenomenon in which the meaning of a word change over time. Most studies on LSC focus on improving the performance of estimating the degree of LSC, however, it is often difficult to interpret how the meaning of a word change. Enhancing the interpretability of LSC is a significant challenge as it could lead to novel insights in this field. To tackle this challenge, we propose a method to map the semantic space of contextualized embeddings of words obtained by a pre-trained language model to a neurobiological feature space. In the neurobiological feature space, each dimension corresponds to a primitive feature of words, and its value represents the intensity of that feature. This enables humans to interpret LSC systematically. When employed for the estimation of the degree of LSC, our method demonstrates superior performance in comparison to the majority of the previous methods. In addition, given the high interpretability of the proposed method, several analyses on LSC are carried out. The results demonstrate that our method not only discovers interesting types of LSC that have been overlooked in previous studies but also effectively searches for words with specific types of LSC.