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 …

Weakly supervised text classification based on a few user-provided seed words has recently
attracted much attention from researchers. Existing methods mainly generate pseudo-labels …

Contextual embeddings represent a new generation of semantic representations learned
from Neural Language Modelling (NLM) that addresses the issue of meaning conflation …

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora
for the task of word sense disambiguation, by exploiting the semantic relationships between …

The issue of whether or not word sense disambiguation (WSD) can improve information
retrieval (IR) results has been intensely debated over the years, with many inconclusive or …

Word sense disambiguation (WSD) finds the actual meaning of a word according to its
context. This paper presents a novel WSD method to find the correct sense of a word present …

Word sense disambiguation (WSD) is a subdomain of natural language processing (NLP).
WSD mainly deals with identifying the correct sense of ambiguous words. The discussed …

Game-theoretic models, thanks to their intrinsic ability to exploit contextual information, have
shown to be particularly suited for the Word Sense Disambiguation task. They represent …

While contextualized embeddings have produced performance breakthroughs in many
Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not …