Continual learning (CL) aims to enable information systems to learn from a continuous data
stream across time. However, it is difficult for existing deep learning architectures to learn a …

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions
that have far‐reaching impact on individuals and society. Their decisions might affect …

Lexical Semantic Change detection, ie, the task of identifying words that change meaning
over time, is a very active research area, with applications in NLP, lexicography, and …

Distributional semantics provides multidimensional, graded, empirically induced word
representations that successfully capture many aspects of meaning in natural languages, as …

Our world is constantly evolving, and so is the content on the web. Consequently, our
languages, often said to mirror the world, are dynamic in nature. However, most current …

Diachronic word embeddings have been widely used in detecting temporal changes.
However, existing methods face the meaning conflation deficiency by representing a word …

State-of-the-art models of lexical semantic change detection suffer from noise stemming from
vector space alignment. We have empirically tested the Temporal Referencing method for …

Pretrained language models based on the transformer architecture have shown great
success in NLP. Textual training data often comes from the web and is thus tagged with time …

Social scientists commonly seek to make statements about how word use varies over
circumstances—including time, partisan identity, or some other document-level covariate …

Word embeddings are increasingly used for the automatic detection of semantic change; yet,
a robust evaluation and systematic comparison of the choices involved has been lacking …