| Challenge: | a new study examines the phenomenon of short-term meaning shift in online communities . the authors use distributional representations to explore the phenomenon . |
| Approach: | They propose to use distributional representations to explore short-term meaning shift in online communities. |
| Outcome: | The proposed model has problems distinguishing meaning shift from referential phenomena, and measures contextual variability to remedy this. |
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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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| Challenge: | Existing models for diachronic and synchronic detection of lexical semantic divergences are superficial and lack of comparison. |
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A Survey of Meaning Representations – From Theory to Practical Utility (2024.naacl-long)
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Exploring Word Usage Change with Continuously Evolving Embeddings (2021.acl-demo)
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Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices (2025.coling-main)
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| Challenge: | Existing methods to analyze word sense proportions are insufficient for understanding semantic shifts . et al., 2018: semantic shift and its effects. |
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Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
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Deep Neural Models of Semantic Shift (N18-1)
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| Challenge: | Diachronic distributional models track changes in word use over time using a continuous variable and a synthetic task to measure the semantic trajectory of a word. |
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