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 …

Temporal word embeddings have been proposed to support the analysis of word meaning
shifts during time and to study the evolution of languages. Different approaches have been …

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 …

The findings from automatic lexical semantic change detection and the models of diachronic
conceptual change are also currently being incorporated in approaches for measuring …

We present a method for detecting word sense changes by utilizing automatically induced
word senses. Our method works on the level of individual senses and allows a word to have …

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 …

Abstract The KubHist Corpus is a massive corpus of Swedish historical newspapers,
digitized by the Royal Swedish library, and available through the Språkbanken corpus …

We investigate some aspects of the history of antisemitism in France, one of the cradles of
modern antisemitism, using diachronic word embeddings. We constructed a large corpus of …

This paper describes EmbLexChange, a system introduced by the “Life-Language” team for
SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes …

Detecting word sense changes can be of great interest in the field of digital humanities. Thus
far, most investigations and automatic methods have been developed and carried out on …