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Found 5 papers, 3 papers with code
What is Done is Done: an Incremental Approach to Semantic Shift Detection
Contextual word embedding techniques for semantic shift detection are receiving more and more attention.
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change
Our evaluation is performed across different languages on eight available benchmarks for LSC, and shows that (i) APD outperforms other approaches for GCD; (ii) XL-LEXEME outperforms other contextualized models for WiC, WSI, and GCD, while being comparable to GPT-4; (iii) there is a clear need for improving the modeling of word meanings, as well as focus on how, when, and why these meanings change, rather than solely focusing on the extent of semantic change.
(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection
In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems.
Incremental Affinity Propagation based on Cluster Consolidation and Stratification
Modern data mining applications require to perform incremental clustering over dynamic datasets by tracing temporal changes over the resulting clusters.
A Survey on Contextualised Semantic Shift Detection
Semantic Shift Detection (SSD) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word.
