Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the
possible change over time in the meanings of a target word. Traditionally, LSC has been …

Modeling lexical semantics is a challenging task, often suffering from interpretability pitfalls.
In this paper, we delve into the generation of dictionary-like sense definitions and explore …

Thus far, the research community has focused on a simplified computational modeling of
semantic change between two time periods. This simplified view has served as a …

The study of semantic shift, that is, of how words change meaning as a consequence of
social practices, events and political circumstances, is relevant in Natural Language …

The society produces textual data online in several ways, eg, via reviews and social media
posts. Therefore, numerous researchers have been working on discovering patterns in …

Large Language Models (LLMs) are trained on Web data that might contain spelling errors
made by humans. But do they become robust to similar real-world noise? In this paper, we …

The meanings and relationships of words shift over time. This phenomenon is referred to as
semantic shift. Research focused on understanding how semantic shifts occur over multiple …

Words change their meaning over time as well as in different contexts. The sense-aware
contextualised word embeddings (SCWEs) such as the ones produced by XL-LEXEME by …

Understanding how large language models (LLMs) grasp the historical context of concepts
and their semantic evolution is essential in advancing artificial intelligence and linguistic …

Abstract Lexical Semantic Change Detection (LSCD) is the task of determining whether a
word has undergone a change in meaning over time. There has been a marked increase in …