Abstract Word Sense Disambiguation (WSD) aims at making explicit the semantics of a word
in context by identifying the most suitable meaning from a predefined sense inventory …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

Contextualized word embeddings have been employed effectively across several tasks in
Natural Language Processing, as they have proved to carry useful semantic information …

Transformer-based architectures brought a breeze of change to Word Sense
Disambiguation (WSD), improving models' performances by a large margin. The fast …

Transformer-based language models have taken many fields in NLP by storm. BERT and its
derivatives dominate most of the existing evaluation benchmarks, including those for Word …

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-
annotated data, hence limiting the power of supervised systems when applied to multilingual …

Abstract Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word
in a given context. It lies at the base of Natural Language Processing as it provides semantic …

Various applications in computational linguistics and artificial intelligence rely on high-
performing word sense disambiguation techniques to solve challenging tasks such as …

Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous
words in the given contexts according to a predefined sense inventory, is one of the most …

We present two supervised (pre-) training methods to incorporate gloss definitions from
lexical resources into neural language models (LMs). The training improves our models' …