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 …

In this paper we present BabelNet–a very large, wide-coverage multilingual semantic
network. The resource is automatically constructed by means of a methodology that …

Most word representation methods assume that each word owns a single semantic vector.
This is usually problematic because lexical ambiguity is ubiquitous, which is also the …

Abstract Word Sense Disambiguation (WSD) systems automatically choose the intended
meaning of a word in context. In this article we present a WSD algorithm based on random …

This paper presents an overview of automatic methods for building domain knowledge
structures (domain models) from text collections. Applications of domain models have a long …

One of the main obstacles to highperformance Word Sense Disambiguation (WSD) is the
knowledge acquisition bottleneck. In this paper, we present a methodology to automatically …

The evaluation of several tasks in lexical semantics is often limited by the lack of large
amounts of manual annotations, not only for training purposes, but also for testing purposes …

Word sense disambiguation (WSD) is a specific task of computational linguistics which aims
at automatically identifying the correct sense of a given ambiguous word from a set of …

This paper presents a new fully automatic method for building highly dense and accurate
knowledge bases from existing semantic resources. Basically, the method uses a wide …