Papers with lexical semantic change

5 papers
Semantic shift in social networks (2021.starsem-1)

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Challenge: lexical semantic change manifests differently across different communities, according to a new study . social network analysis is a tool of sociolinguists studying variation and change .
Approach: They use distributional methods to quantify lexical semantic change and induce a social network on communities based on interactions between members.
Outcome: The proposed method is based on interactions between members and the community.
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change (P19-1)

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Challenge: State-of-the-art lexical semantic change detection models suffer from noise stemming from vector space alignment.
Approach: They propose a method to simulate lexical semantic change and control for possible biases by avoiding alignment.
Outcome: The proposed method outperforms state-of-the-art models on a synthetic task and a manual testset.
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.
Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)

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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.
Multilinguality Does not Make Sense: Investigating Factors Behind Zero-Shot Cross-Lingual Transfer in Sense-Aware Tasks (2025.emnlp-main)

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Challenge: Cross-lingual transfer allows models to perform tasks in languages unseen during training and is often assumed to benefit from increased multilinguality.
Approach: They challenge this assumption by analyzing polysemy disambiguation and lexical semantic change in 28 languages and using confounding factors to account for perceived advantages.
Outcome: The proposed models and benchmarks are compared across 28 languages and show that multilingual training is neither necessary nor beneficial for effective transfer.

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