A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection (2024.findings-acl)
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| Challenge: | Existing Word-in-Context (WiC) datasets are used to detect temporal semantic changes of words. |
| Approach: | They propose a supervised two-staged SCD method that uses existing Word-in-Context (WiC) datasets to predict temporal semantic changes of words. |
| Outcome: | The proposed method achieves strong performance in multiple languages and significant improvements on WiC benchmarks. |
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| Challenge: | Existing methods for lexical semantic-change detection quantify changes in the meaning of words over time. |
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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. |
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Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping (2023.findings-emnlp)
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| Challenge: | Detecting semantic changes of words is an important task for various NLP applications that must make time-sensitive predictions. |
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| Challenge: | Lexical semantic change detection is a new and innovative research field. |
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A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains (P19-1)
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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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Lexical Semantic Change Discovery (2021.acl-long)
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| Challenge: | Existing approaches to Lexical Semantic Change Detection are limited. |
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