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

In 2004 we published in this journal an article describing OntoLearn, one of the first systems
to automatically induce a taxonomy from documents and Web sites. Since then, OntoLearn …

Transformer-based architectures brought a breeze of change to Word Sense
Disambiguation (WSD), improving models' performances by a large margin. The fast …

Deriving prior polarity lexica for sentiment analysis-where positive or negative scores are
associated with words out of context-is a challenging task. Usually, a trade-off between …

Ambiguity in natural-language requirements is a pervasive issue that has been studied by
the requirements engineering community for more than two decades. A fully manual …

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 …

One of the weaknesses of current supervised word sense disambiguation (WSD) systems is
that they only treat a word as a discrete entity. However, a continuous-space representation …

Assigning a positive or negative score to a word out of context (ie a word's prior polarity) is a
challenging task for sentiment analysis. In the literature, various approaches based on …

Most work on word sense disambiguation has assumed that word usages are best labeled
with a single sense. However, contextual ambiguity or fine-grained senses can potentially …

This article presents a new model for word sense disambiguation formulated in terms of
evolutionary game theory, where each word to be disambiguated is represented as a node …