| Challenge: | Using word embeddings, we study sudden semantic shifts that occur when a sudden event radically changes public opinion on a topic. |
| Approach: | They use word embeddings to study how Twitter associations evolve . they find domain knowledge and data selection are of prime importance when using word embeds to understand semantic shifts. |
| Outcome: | The proposed method validates associations on Twitter with NATO in real-world events but is difficult to distinguish between noise and real-time signals. |
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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. |
| Approach: | They propose several axes along which these methods can be compared and propose a framework for comparison. |
| Outcome: | The proposed methods are compared with existing methods and outline their main challenges and potential applications. |
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. |
| Outcome: | The proposed method is comparable to state-of-the-art on diachrony tasks, but it does not translate to practical value in detecting new semantic shifts. |
Computational modeling of semantic change (2024.eacl-tutorials)
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| Challenge: | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. |
| Approach: | They propose to categorise the types of change, the causes and the mechanisms underlying the different types of changes using large diachronic corpora and evaluation benchmarks. |
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Revealing COVID-19’s Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter (2024.findings-emnlp)
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| Challenge: | Social media data provide a new source for social science and cultural analysis research, but its analysis is challenging due to the semantic shift phenomenon, where word meanings evolve over time. |
| Approach: | They propose an unsupervised dynamic word embedding method to capture longitudinal semantic shifts in social media data without predefined anchor words. |
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Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models (2023.findings-emnlp)
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| Challenge: | a study comparing static and contextualized language models for subtle semantic shifts in Dutch and English shows that they can detect political connotations and associations. |
| Approach: | They propose a method for detecting subtle semantic shifts between political communities in Dutch and English using static and contextualized language models. |
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Understanding Computational Models of Semantic Change: New Insights from the Speech Community (2023.emnlp-main)
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| Challenge: | Using type-level and token-level word embeddings, we obtain semantic change estimates from type-based models and empirical linguistic properties. |
| Approach: | They analyze 40 target words with type-level and token-level word embeddings, empirical linguistic properties, and speaker-provided acceptability ratings and qualitative remarks. |
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Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups (2025.findings-acl)
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| Challenge: | Existing attempts to model the relationship between the real world and written or spoken text have focused on more interpretable and simplistic text representations. |
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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. |
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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. |
| Approach: | They propose a way to track word usage changes via continuously evolving embeddings . they demonstrate an interactive web app that can explore semantic shifts with interactive plots a text . |
| Outcome: | The proposed method can be used to analyze word usage changes with interactive plots. |
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. |
| Approach: | They propose three variants of sequential models for detecting semantically shifted words . they demonstrate that temporal modelling of word representations yields a clear-cut advantage . |
| Outcome: | The proposed models account for the changes in word representations over time. |