Recent years have witnessed a surge of publications aimed at tracing temporal changes in
lexical semantics using distributional methods, particularly prediction-based word …

Words shift in meaning for many reasons, including cultural factors like new technologies
and regular linguistic processes like subjectification. Understanding the evolution of …

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 …

We perform an interdisciplinary large-scale evaluation for detecting lexical semantic
divergences in a diachronic and in a synchronic task: semantic sense changes across time …

We propose a new method that leverages contextual embeddings for the task of diachronic
semantic shift detection by generating time specific word representations from BERT …

Our languages are in constant flux driven by external factors such as cultural, societal and
technological changes, as well as by only partially understood internal motivations. Words …

Despite the predominance of contextualized embeddings in NLP, approaches to detect
semantic change relying on these embeddings and clustering methods underperform …

Languages are continuously undergoing changes, and the mechanisms that underlie these
changes are still a matter of debate. In this work, we approach language evolution through …

While summarization has been extensively researched in natural language processing
(NLP), cross-lingual cross-temporal summarization (CLCTS) is a largely unexplored area …

In this paper, we describe our method for the detection of lexical semantic change, ie, word
sense changes over time. We examine semantic differences between specific words in two …