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

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 …

Polysemy is the type of lexical ambiguity where a word has multiple distinct but related
interpretations. In the past decade, it has been the subject of a great many studies across …

Recent work has shown that the integration of visual information into text-based models can
substantially improve model predictions, but so far only visual information extracted from …

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

Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

We propose a framework that extends synchronic polysemy annotation to diachronic
changes in lexical meaning, to counteract the lack of resources for evaluating computational …

This paper investigates contextual language models, which produce token representations,
as a resource for lexical semantics at the word or type level. We construct multi-prototype …

Word embeddings are widely used in Natural Language Processing, mainly due to their
success in capturing semantic information from massive corpora. However, their creation …

Word meaning is notoriously difficult to capture, both synchronically and diachronically. In
this paper, we describe the creation of the largest resource of graded contextualized …