# Challenges for Computational Lexical Semantic Change

arXiv: https://arxiv.org/abs/2101.07668 · HTML: https://mlsanity.com/paper/2101.07668/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 120 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [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)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing](https://mlsanity.com/paper/1807.00914/) (2018)
- [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)
- [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://mlsanity.com/paper/1804.06517/) (2018)
- [Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change](https://mlsanity.com/paper/1906.01688/) (2019)
- [Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution](https://mlsanity.com/paper/1501.00960/) (2015)
- [On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models](https://mlsanity.com/paper/1704.02497/) (2017)
- [GASC: Genre-Aware Semantic Change for Ancient Greek](https://mlsanity.com/paper/1903.05587/) (2019)
- [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)
- [German in Flux: Detecting Metaphoric Change via Word Entropy](https://mlsanity.com/paper/1706.04971/) (2017)
- [Semantic Change and Semantic Stability: Variation is Key](https://mlsanity.com/paper/1906.05760/) (2019)
- [Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants](https://mlsanity.com/paper/1707.08660/) (2017)
- [Detecting New Word Meanings: A Comparison of Word Embedding Models in Spanish](https://mlsanity.com/paper/2001.05285/) (2020)

## Built on by (most cited first, 16 of 16)

- [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)
- [Contextualized language models for semantic change detection: lessons learned](https://mlsanity.com/paper/2209.00154/) (2022)
- [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)
- [Survey in Characterizing Semantic Change](https://mlsanity.com/paper/2402.19088/) (2024)
- [Semantic Change Characterization with LLMs using Rhetorics](https://mlsanity.com/paper/2407.16624/) (2024)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)
- [Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection](https://mlsanity.com/paper/2101.09368/) (2021)
- [From Plenipotentiary to Puddingless: Users and Uses of New Words in Early English Letters](https://mlsanity.com/paper/2103.09926/) (2021)
- [A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications](https://mlsanity.com/paper/2406.06052/) (2024)
- [AXOLOTL'24 Shared Task on Multilingual Explainable Semantic Change Modeling](https://mlsanity.com/paper/2407.04079/) (2024)
- [LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data](https://mlsanity.com/paper/2503.08042/) (2025)
- [Hybrid Retrieval-Augmented Generation for Robust Multilingual Document Question Answering](https://mlsanity.com/paper/2512.12694/) (2025)
- [From cart to truck: meaning shift through words in English in the last two centuries](https://mlsanity.com/paper/2408.16209/) (2024)
- [The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usage](https://mlsanity.com/paper/2606.16560/) (2026)

## Abstract (arXiv)

The computational study of lexical semantic change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics. Most of the research so far has focused on methods for modelling and detecting semantic change using large diachronic textual data, with the majority of the approaches employing neural embeddings. While methods that offer easy modelling of diachronic text are one of the main reasons for the spiking interest in LSC, neural models leave many aspects of the problem unsolved. The field has several open and complex challenges. In this chapter, we aim to describe the most important of these challenges and outline future directions.