Neural architectures are the current state of the art in Word Sense Disambiguation (WSD).
However, they make limited use of the vast amount of relational information encoded in …

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 …

The ability to learn from large unlabeled corpora has allowed neural language models to
advance the frontier in natural language understanding. However, existing self-supervision …

A major obstacle in Word Sense Disambiguation (WSD) is that word senses are not
uniformly distributed, causing existing models to generally perform poorly on senses that are …

Contextual representations of words derived by neural language models have proven to
effectively encode the subtle distinctions that might occur between different meanings of the …

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 …

We present minicons, an open source library that provides a standard API for researchers
interested in conducting behavioral and representational analyses of transformer-based …

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 …