Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

We present an automatic approach to the construction of BabelNet, a very large, wide-
coverage multilingual semantic network. Key to our approach is the integration of …

Word sense disambiguation (WSD) is a process used to determine the most appropriate
meaning of a word in a given contextual framework, particularly when the word is …

Linguistic typology aims to capture structural and semantic variation across the world's
languages. A large-scale typology could provide excellent guidance for multilingual Natural …

We present a multilingual joint approach to Word Sense Disambiguation (WSD). Our method
exploits BabelNet, a very large multilingual knowledge base, to perform graphbased WSD …

We start with an overview of a class of submodular functions called SCMMs (sums of
concave composed with non-negative modular functions plus a final arbitrary modular). We …

Contextual representations of words built using neural language models have been shown
to successfully store minor differences between multiple meanings of the same word …

Performance of word sense disambiguation (WSD) is one of the challenging tasks in the
area of natural language processing (NLP). Generation of sense annotated corpus for …

In this paper, we present a new method based on co-occurrence graphs for performing
Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises …

The choice of the dictionary that provides the possible translations a system has to choose
when performing Cross-Lingual Word Sense Disambiguation (CLWSD) is one of the most …