The course Computational methods for Lexical Semantic Change Detection will be offered at the Lectures on Computational Linguistics 2024 of the Italian Association of Computational Linguistics (AILC). It is scheduled to take place in Bari, Italy, from June 19th to June 21th.
This lecture will introduce computational modeling of semantic change (also called lexical semantic change), a phenomenon associated with the diachronic change of the meaning of words. Semantic change can be subtle while still having a large impact on interpretation of text. For example, not knowing that the English word girl referred to young people of either gender in a specific text changes the way in which we interpret the content. This lecture will provide an overview of the problem of semantic change and its consequences; introduction to computational current approaches, problems, and challenges in detecting lexical semantic changes; and finally, evaluation of the results.
The laboratory will provide a foundational overview of Distributional Semantics, highlighting the significance of static embeddings and BERT-based models in understanding the lexical semantic change. It will encompass both unsupervised approaches, such as similarity measures and clustering algorithms, and supervised approaches that utilize temporal information. Additionally, the laboratory will include hands-on session on Lexical Semantic Change (LSC) models, Diachronic Word Usage Graphs (DWUGs), and evaluations strategies. The discussion will extend to models based on Word Sense Disambiguation and generative models, offering a comprehensive understanding of the current methodologies and their applications in detecting and analyzing semantic change.
References:
- Nina Tahmasebi, Adam Jatowt, Lars Borin. Survey of Computational Approaches to Lexical Semantic Change Detection. Nina Tahmasebi, Lars Borin, Adam Jatowt, Yang Xu, Simon Hengchen (eds). Computational Approaches to Semantic Change. Berlin: Language Science Press.
- Stefano Montanelli, Francesco Periti. A Survey on Contextualised Semantic Shift Detection. arXiv preprint arXiv:2304.01666.
- Dubossarsky, H., Weinshall, D., & Grossman, E. (2017, September). Outta control: Laws of semantic change and inherent biases in word representation models. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 1136-1145).
- Geeraerts, D. (2020). Semantic Change. In The Wiley Blackwell Companion to Semantics (eds D. Gutzmann, L. Matthewson, C. Meier, H. Rullmann and T. Zimmermann).
- Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg, Haim Dubossarsky. Challenges for Computational Lexical Semantic Change. Nina Tahmasebi, Lars Borin, Adam Jatowt, Yang Xu, Simon Hengchen (eds). Computational Approaches to Semantic Change. Berlin: Language Science Press.
- Pierluigi Cassotti, Lucia Siciliani, Marco DeGemmis, Giovanni Semeraro, Pierpaolo Basile, XL-LEXEME: WiC Pretrained Model for Cross-Lingual LEXical sEMantic changE. (2023) In Proc. of ACL2023
- Francesco Periti and Nina Tahmasebi. A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change. (2024) arXiv:2402.12011.
- Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, and Barbara McGillivray. DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages. . In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7079–7091, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.