| Challenge: | Modern language models can contextualize words based on their surrounding contexts, but semantic change can compromise this capability. |
| Approach: | They propose a replacement schema where a target word is replaced with lexical replacements of varying relatedness . they leverage the replacement schema as a basis for a novel interpretable model for semantic change . |
| Outcome: | The proposed model is the first to evaluate LLaMa for semantic change detection . it shows that lexical replacements can detect unexpected contexts . |
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| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
| Approach: | They propose a method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics. |
| Outcome: | The proposed method captures a variety of synchronic and diachronic linguistic phenomena and is highly reproducible and reproducible. |
Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection (2021.eacl-main)
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| Challenge: | Lexical semantic change detection is a new and innovative research field. |
| Approach: | They propose to pre-train on large corpora and refine on diachronic target corpors to improve performance. |
| Outcome: | The proposed models improve on large corpora and diachronic target corpors . the proposed models are compared with existing models in a variety of learning scenarios . |
Substitution-based Semantic Change Detection using Contextual Embeddings (2023.acl-short)
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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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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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Using Synchronic Definitions and Semantic Relations to Classify Semantic Change Types (2024.acl-long)
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| Challenge: | Existing models for detecting semantic change in corpora have been disregarded due to lack of knowledge of the nature of semantic change and the way it takes place. |
| Approach: | They propose a model that leverages synchronic lexical relations and definitions of word meanings to detect these types of change. |
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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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Toward Sentiment Aware Semantic Change Analysis (2024.eacl-srw)
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| Challenge: | Current approaches to analyze semantic change are lagging behind . current methods only detect semantic change as a binary classification or graded change scores . |
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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 . |
| Outcome: | The proposed model outperforms other models on eight available benchmarks for Lexical Semantic Change (LSC) while comparable to GPT-4. |
Lexical Semantic Change Discovery (2021.acl-long)
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| Challenge: | Existing approaches to Lexical Semantic Change Detection are limited. |
| Approach: | They propose a shift from change detection to change discovery by fine-tuning a type-based and a token-based approach on recently published German data. |
| Outcome: | The proposed models can be applied to discover new words undergoing meaning change from the full corpus vocabulary. |
A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications (2024.acl-long)
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| Challenge: | Historical linguists have identified multiple forms of lexical semantic change. |
| Approach: | They propose a framework for integrating and evaluating lexical semantic changes in historical linguists and a unified computational methodology for evaluating them concurrently. |
| Outcome: | The proposed framework enables lexical semantic change to be mapped economically and systematically and has applications in computational social science. |