Computational modeling of semantic change (2024)

Abstract: Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. In particular, words can undergo the process of semantic change, which can be subtle and significantly impact the interpretation of texts. For example, the word terrific used to mean ‘causing terror’ and was as such synonymous to terrifying. Nowadays, speakers use the word in the sense of ‘excessive’ and even ‘amazing’. In Historical Linguistics, tools and methods have been developed to analyse this phenomenon, including systematic categorisations of the types of change, the causes and the mechanisms underlying the different types of change. However, traditional linguistic methods, while informative, are often based on small, carefully curated samples. Thanks to the availability of both large diachronic corpora, the computational means to model word meaning unsupervised, and evaluation benchmarks, we are seeing an increasing interest in the computational modelling of semantic change. This is evidenced by the increasing number of publications in this new domain as well as the organisation of initiatives and events related to this topic, such as four editions of the International Workshop on Computational Approaches to Historical Language Change LChange1, and several evaluation campaigns (Schlechtweg et al., 2020a; Basile et al., 2020b; Kutuzov et al.; Zamora-Reina et al., 2022).

## introduction languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. in particular, words can undergo the process of semantic change, which can be subtle yet significantly impact the interpretation of texts. for example, the word terrific used to mean "causing terror" and was as such synonymous to terrifying. nowadays, speakers use the word in the sense of "excessive" and even "amazing". in historical linguistics, tools and methods have been developed to analyse this phenomenon, including systematic categorisations of the types of change, the causes and the mechanisms underlying the different types of change. however, traditional linguistic methods, while informative, are often based on small, carefully curated samples. thanks to the availability of both large diachronic corpora, the tools to model word meaning using unsupervised computational methods, and evaluation benchmarks, we are seeing an increasing interest in the computational modelling of semantic change. this is evidenced by the increasing number of publications in this new domain as well as the organisation of initiatives and events related to this topic, such as the yearly workshop on computational approaches to historical language change lchange foot_0 that reached its fourth year , and several evaluation campaigns @xcite @xcite . relevance computational modelling of semantic change is highly relevant for fields like lexicography but also studies in (historical) linguistics where we can complement and verify existing research on larger corpora, more genres, more ex-tended periods and many more languages. computational modelling of semantic change is also interesting for any text-based humanities and social sciences as well as technical and medical science, where the evolution of concepts or the progression of before and after is studied. in the past few years, we have seen an increasing interest in utilizing methods for semantic change in other domains. @xcite delved into the connections between "isms" (like liberalism, socialism, and conservatism) and ideological language, shedding light on the progression of political language throughout history. bizzoni et al. (2020) this tutorial will be interest of for the acl community as a venue for facilitating discussions and sharing knowledge on diachronic linguistics and time-aware language analysis. there is an extensive collection of models, methods and trained diachronic resources that benefit anyone interested in temporally evolving information beyond the lsc community. moreover, it will provide a practical demonstration of available tools to researchers and practitioners working on different aspects of lsc and historical linguistics. in particular, we ## tutorial overview this tutorial will overview the current approaches, problems, and challenges in detecting lexical semantic changes. at its core, the computational modelling of semantic change consists of the following: ## tutorial information type of the tutorial introductory. length this is a 6-hour tutorial. target audience and background this tutorial targets researchers at different levels of expertise in the field. introductory researchers will gain a comprehensive understanding of the topic, covering foundational concepts and available resources. intermediate researchers will deepen their knowledge with advanced approaches for automatic detection and analysis of lsc, while advanced researchers will explore state-of-the-art techniques and address complex challenges. the tutorial is designed to be inclusive, fostering the participation of attendees with varying experience levels. furthermore, the tutorial aims to foster a more powerful synergy between the lsc domain and other areas of nlp, particularly emphasising the integration with lexical semantics and research pursuits in word sense discrimination. prerequisites include a basic understanding of linguistics, natural language processing, and computational linguistics concepts.