Challenges for Computational Lexical Semantic Change
It highlights critical problems for researchers in computational linguistics and AI, focusing on incremental improvements in modeling semantic change.
The paper identifies key unsolved challenges in computational lexical semantic change, such as limitations of neural models, and outlines future research directions to address these issues.
The computational study of lexical semantic change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics. Most of the research so far has focused on methods for modelling and detecting semantic change using large diachronic textual data, with the majority of the approaches employing neural embeddings. While methods that offer easy modelling of diachronic text are one of the main reasons for the spiking interest in LSC, neural models leave many aspects of the problem unsolved. The field has several open and complex challenges. In this chapter, we aim to describe the most important of these challenges and outline future directions.