Abstract Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical
ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental …

We develop a learning principle and an efficient algorithm for batch learning from logged
bandit feedback. This learning setting is ubiquitous in online systems (eg, ad placement …

The focus of data scientists is essentially divided into three areas: collecting data, analyzing
data, and inferring information from data. Each one of these tasks requires special …

In this paper we present the Multilingual All-Words Sense Disambiguation and Entity Linking
task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known problems …

Word sense disambiguation is necessary in translation because different word senses often
have different translations. Neural machine translation models learn different senses of …

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 …

Spectral learning algorithms have recently become popular in data-rich domains, driven in
part by recent advances in large scale randomized SVD, and in spectral estimation of …

Despite the remarkable progress made in the field of Machine Translation (MT), current
systems still struggle when translating ambiguous words, especially when these express …

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 …

Semantic representation lies at the core of several applications in Natural Language
Processing. However, most existing semantic representation techniques cannot be used …