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 …

OpenAI has released the Chat Generative Pre-trained Transformer (ChatGPT) and
revolutionized the approach in artificial intelligence to human-model interaction. The first …

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 …

Over the last two decades, determining the similarity between words as well as between
their meanings, that is, word senses, has been proven to be of vital importance in the field of …

Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018),
Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in …

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 …

Abstract Word Sense Disambiguation is a long-standing task in Natural Language
Processing, lying at the core of human language understanding. However, the evaluation of …

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 …