Languages change constantly over time, influenced by social, technological, cultural, and political factors that alter our understanding of texts. New NLP/ML models can detect words that have changed their meaning. They help not only to understand old texts, but meaning changes across disciplines, the linguistic and social causes behind historical language change, changing views in political science and psychology, and changing styles in poetry and scientific writing. I will provide an overview of these computational models, describing changes in word categories and their implications for lexical evolution.

 

References

Cassotti, P., Periti, F., De Pascale, S., Dubossarsky, H., & Tahmasebi, N. (2024). Computational modeling of semantic change. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts (pp. 1-8).

Periti, F., Cassotti, P., Dubossarsky, H., & Tahmasebi, N. (2024). Analyzing Semantic Change through Lexical Replacements. arXiv preprint arXiv:2404.18570.

Hahamy, A., Dubossarsky, H., & Behrens, T. E. (2023). The human brain reactivates context-specific past information at event boundaries of naturalistic experiences. Nature Neuroscience, 26(6), 1080-1089.

Dubossarsky, H., Tsvetkov, Y., Dyer, C., & Grossman, E. (2015). A bottom up approach to category mapping and meaning change. In NetWordS (pp. 66-70).