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 …

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

The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI.
However, when it comes to Natural Language Processing (NLP), symbols have to be …

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

Meaning Representation (AMR) is a popular formalism of natural language that represents
the meaning of a sentence as a semantic graph. It is agnostic about how to derive meanings …

Abstract Recent studies treat Word Sense Disambiguation (WSD) as a single-label
classification problem in which one is asked to choose only the best-fitting sense for a target …

Recent research indicates that taking advantage of complex syntactic features leads to
favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of …

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 …

Recently, generative approaches have been used effectively to provide definitions of words
in their context. However, the opposite, ie, generating a usage example given one or more …

Abstract Word Sense Disambiguation (WSD), ie, the task of assigning senses to words in
context, has seen a surge of interest with the advent of neural models and a considerable …