# Survey in Characterizing Semantic Change arXiv: https://arxiv.org/abs/2402.19088 ยท HTML: https://mlsanity.com/paper/2402.19088/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 131 references that are in the map. - First posted on arXiv 29 Feb 2024. ## Builds on (most cited first, 30 of 40) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021) - [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022) - [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021) - [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022) - [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017) - [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019) - [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019) - [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020) - [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016) - [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020) - [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) - [Rapid Adaptation of Neural Machine Translation to New Languages](https://mlsanity.com/paper/1808.04189/) (2018) - [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018) - [From Word to Sense Embeddings: A Survey on Vector Representations of Meaning](https://mlsanity.com/paper/1805.04032/) (2018) - [MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://mlsanity.com/paper/2104.13615/) (2021) - [Are All Good Word Vector Spaces Isomorphic?](https://mlsanity.com/paper/2004.04070/) (2020) - [A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains](https://mlsanity.com/paper/1906.02979/) (2019) - [Structured Sentiment Analysis as Dependency Graph Parsing](https://mlsanity.com/paper/2105.14504/) (2021) - [English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings](https://mlsanity.com/paper/2211.06127/) (2022) - [Contextualized language models for semantic change detection: lessons learned](https://mlsanity.com/paper/2209.00154/) (2022) - [Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://mlsanity.com/paper/1606.02820/) (2016) - [Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis](https://mlsanity.com/paper/2305.11993/) (2023) - [Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods](https://mlsanity.com/paper/2001.07740/) (2020) - [Challenges for Computational Lexical Semantic Change](https://mlsanity.com/paper/2101.07668/) (2021) ## Built on by (most cited first, 5 of 5) - [Analyzing Semantic Change through Lexical Replacements](https://mlsanity.com/paper/2404.18570/) (2024) - [Semantic Change Characterization with LLMs using Rhetorics](https://mlsanity.com/paper/2407.16624/) (2024) - [A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications](https://mlsanity.com/paper/2406.06052/) (2024) - [Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices](https://mlsanity.com/paper/2501.09538/) (2025) - [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) ## Abstract (arXiv) Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words) or semantic changes of words (new meaning to existing words). Understanding the meaning of words is vital for interpreting texts coming from different cultures (regionalism or slang), domains (e.g., technical terms), or periods. In computer science, these words are relevant to computational linguistics algorithms such as translation, information retrieval, question answering, etc. Semantic changes can potentially impact the quality of the outcomes of these algorithms. Therefore, it is important to understand and characterize these changes formally. The study of this impact is a recent problem that has attracted the attention of the computational linguistics community. Several approaches propose methods to detect semantic changes with good precision, but more effort is needed to characterize how the meaning of words changes and to reason about how to reduce the impact of semantic change. This survey provides an understandable overview of existing approaches to the textitcharacterization of semantic changes and also formally defines three classes of characterizations: if the meaning of a word becomes more general or narrow (change in dimension) if the word is used in a more pejorative or positive/ameliorated sense (change in orientation), and if there is a trend to use the word in a, for instance, metaphoric or metonymic context (change in relation). We summarized the main aspects of the selected publications in a table and discussed the needs and trends in the research activities on semantic change characterization.