Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings (D19-1)
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| Challenge: | Word embeddings are increasingly used for automatic detection of semantic change, but a robust evaluation and systematic comparison of the choices involved has been lacking. |
| Approach: | They propose a new evaluation framework for semantic change detection using whole time series and a Twitter dataset spanning 5.5 years. |
| Outcome: | The proposed framework shows that using whole time series is preferable over continuously trained embeddings for long time periods and that the reference point matters. |
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| Challenge: | a simplified approach to measuring semantic change using contextual embeddings is proposed . the static word vectors used for measuring semantic changes are difficult to interpret . |
| Approach: | They propose a simplified approach to measuring semantic change using contextual embeddings . they use the Jensen-Shannon Divergence between the distributions of most probable replacements for masked words in different time periods to measure semantic change. |
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A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change (2024.naacl-long)
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| Challenge: | Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC) current evaluations focus on a specific task known as Graded Change Detection (GCD) however, performance comparisons between different approaches are often misleading due to diverse settings. |
| Approach: | They evaluate the performance of contextualized embeddings for Lexical Semantic Change (LSC) they break the problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks . |
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Diachronic word embeddings and semantic shifts: a survey (C18-1)
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| Challenge: | Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing. |
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Scalable and Interpretable Semantic Change Detection (2021.naacl-main)
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| Challenge: | Several cluster-based methods for word usage change detection are unscalable in terms of memory consumption and computation time. |
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Sequential Modelling of the Evolution of Word Representations for Semantic Change Detection (2020.emnlp-main)
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| Challenge: | Existing models that detect semantically shifted words do not account for its evolution through time. |
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Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora (2020.acl-main)
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| Challenge: | comparing two corpus texts and searching for words that differ in their usage between them is a common problem in digital humanities and computational social science. |
| Approach: | They propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word. |
| Outcome: | The proposed method is interpretable and stable in 9 different setups and is highly reliable. |
Exploring Word Usage Change with Continuously Evolving Embeddings (2021.acl-demo)
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| Challenge: | a new method to track word usage changes is proposed for text datasets that are collected over a longer period of time. |
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Current Semantic-change Quantification Methods Struggle with Semantic Change Discovery in the Wild (2025.emnlp-main)
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| Challenge: | Existing methods for lexical semantic-change detection quantify changes in the meaning of words over time. |
| Approach: | They propose to use a top-k setup to evaluate semantic-change discovery despite lacking complete annotations on a battery of semantic-changing detection methods. |
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Detecting Contact-Induced Semantic Shifts: What Can Embedding-Based Methods Do in Practice? (2021.emnlp-main)
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| Challenge: | Existing work on semantic change detection methods has focused on generic research questions and datasets, using them as a training ground for proof-of-concept studies. |
| Approach: | They propose to use type-level embeddings to detect new semantic shifts and token-level embeddeds to isolate regionally specific occurrences. |
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Graph-based Clustering for Detecting Semantic Change Across Time and Languages (2024.eacl-long)
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| Challenge: | Existing approaches to detect semantic change using contextualized embeddings are underperforming . a graph-based clustering approach captures nuanced changes in word senses across time and languages . |
| Approach: | They propose a graph-based clustering approach to capture nuanced changes in word senses across time and languages. |
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