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

Despite its importance, the time variable has been largely neglected in the NLP and
language model literature. In this paper, we present TimeLMs, a set of language models …

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

This paper presents the first unsupervised approach to lexical semantic change that makes
use of contextualised word representations. We propose a novel method that exploits the …

Our world is open-ended, non-stationary, and constantly evolving; thus what we talk about
and how we talk about it change over time. This inherent dynamic nature of language …

Keeping the performance of language technologies optimal as time passes is of great
practical interest. We study temporal effects on model performance on downstream …

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 …

Pre-trained language models (LMs) encode rich information about linguistic structure but
their knowledge about lexical polysemy remains unclear. We propose a novel experimental …