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 …

Supervised systems have nowadays become the standard recipe for Word Sense
Disambiguation (WSD), with Transformer-based language models as their primary …

In communication, textual data are a vital attribute. In all languages, ambiguous or
polysemous words' meaning changes depending on the context in which they are used. The …

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 …

We present the first shared task on semantic change discovery and detection in Spanish and
create the first dataset of Spanish words manually annotated for semantic change using the …

With state-of-the-art systems having finally attained estimated human performance, Word
Sense Disambiguation (WSD) has now joined the array of Natural Language Processing …

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

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 …

The emergent cross-lingual transfer seen in multilingual pretrained models has sparked
significant interest in studying their behavior. However, because these analyses have …

Named entities–typically expressed via proper nouns–play a key role in Natural Language
Processing, as their identification and comprehension are crucial in tasks such as Relation …