# Lexical Semantic Change Discovery arXiv: https://arxiv.org/abs/2106.03111 ยท HTML: https://mlsanity.com/paper/2106.03111/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 73 references that are in the map. - First posted on arXiv 6 Jun 2021. ## Builds on (most cited first, 27 of 27) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019) - [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) - [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018) - [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) - [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014) - [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://mlsanity.com/paper/1804.06517/) (2018) - [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) - [A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains](https://mlsanity.com/paper/1906.02979/) (2019) - [DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages](https://mlsanity.com/paper/2104.08540/) (2021) - [Capturing Evolution in Word Usage: Just Add More Clusters?](https://mlsanity.com/paper/2001.06629/) (2020) - [RuSemShift: a dataset of historical lexical semantic change in Russian](https://mlsanity.com/paper/2010.06436/) (2020) - [Challenges for Computational Lexical Semantic Change](https://mlsanity.com/paper/2101.07668/) (2021) - [GASC: Genre-Aware Semantic Change for Ancient Greek](https://mlsanity.com/paper/1903.05587/) (2019) - [CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations](https://mlsanity.com/paper/2005.06602/) (2020) - [Simulating Lexical Semantic Change from Sense-Annotated Data](https://mlsanity.com/paper/2001.03216/) (2020) - [IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection](https://mlsanity.com/paper/2008.03164/) (2020) - [OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection](https://mlsanity.com/paper/2011.03258/) (2020) - [CL-IMS @ DIACR-Ita: Volente o Nolente: BERT does not outperform SGNS on Semantic Change Detection](https://mlsanity.com/paper/2011.07247/) (2020) - [Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection](https://mlsanity.com/paper/2101.09368/) (2021) - [UWB @ DIACR-Ita: Lexical Semantic Change Detection with CCA and Orthogonal Transformation](https://mlsanity.com/paper/2011.14678/) (2020) ## Built on by (most cited first, 13 of 13) - [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023) - [Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change](https://mlsanity.com/paper/2210.17127/) (2022) - [LSCDiscovery: A shared task on semantic change discovery and detection in Spanish](https://mlsanity.com/paper/2205.06691/) (2022) - [Substitution-based Semantic Change Detection using Contextual Embeddings](https://mlsanity.com/paper/2309.02403/) (2023) - [Detection of Non-recorded Word Senses in English and Swedish](https://mlsanity.com/paper/2403.02285/) (2024) - [The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks](https://mlsanity.com/paper/2404.00176/) (2024) - [Comparative Analysis of Static and Contextual Embeddings for Analyzing Semantic Changes in Medieval Latin Charters](https://mlsanity.com/paper/2410.09283/) (2024) - [DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modelling](https://mlsanity.com/paper/2602.11968/) (2026) - [JuniperLiu at CoMeDi Shared Task: Models as Annotators in Lexical Semantics Disagreements](https://mlsanity.com/paper/2411.12147/) (2024) - [FuocChuVIP123 at CoMeDi Shared Task: Disagreement Ranking with XLM-Roberta Sentence Embeddings and Deep Neural Regression](https://mlsanity.com/paper/2501.12336/) (2025) - [Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings](https://mlsanity.com/paper/2506.13569/) (2025) - [XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification](https://mlsanity.com/paper/2507.14578/) (2025) - [Rethinking Metrics for Lexical Semantic Change Detection](https://mlsanity.com/paper/2602.15716/) (2026) ## Abstract (arXiv) While there is a large amount of research in the field of Lexical Semantic Change Detection, only few approaches go beyond a standard benchmark evaluation of existing models. In this paper, we propose a shift of focus from change detection to change discovery, i.e., discovering novel word senses over time from the full corpus vocabulary. By heavily fine-tuning a type-based and a token-based approach on recently published German data, we demonstrate that both models can successfully be applied to discover new words undergoing meaning change. Furthermore, we provide an almost fully automated framework for both evaluation and discovery.