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

The knowledge acquisition bottleneck strongly affects the creation of multilingual sense-
annotated data, hence limiting the power of supervised systems when applied to multilingual …

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 …

Lexical ambiguity can impede NLP systems from accurate understanding of semantics.
Despite its potential benefits, the integration of sense-level information into NLP systems has …

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from
Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences …

Abstract Word Sense Disambiguation (WSD) is the task of associating the correct meaning
with a word in a given context. WSD provides explicit semantic information that is beneficial …

For many low-resource languages, the only available language models are large
multilingual models trained on many languages simultaneously. However, using FLORES …

In this work, we introduce EMMA-500, a large-scale multilingual language model continue-
trained on texts across 546 languages designed for enhanced multilingual performance …

Large sense-annotated datasets are increasingly necessary for training deep supervised
systems in Word Sense Disambiguation. However, gathering high-quality sense-annotated …

Purpose This paper aims to propose a statistical and context-aware feature reduction
algorithm that improves sentiment classification accuracy. Classification of reviews with …