The ambitious goal of this work is to develop a cross-lingual name tagging and linking
framework for 282 languages that exist in Wikipedia. Given a document in any of these …

We automatically create enormous, free and multilingual silver-standard training annotations
for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner …

Wikipedia has become one of the ten most visited sites on the Web, and the world's leading
source of Web reference information. Its rapid success has inspired hundreds of scholars …

Recent years have seen a great deal of work that exploits collaborative, semi-structured
content for Artificial Intelligence (AI) and Natural Language Processing (NLP). This special …

Named entity recognition (NER) is used in many domains beyond the newswire text that
comprises current gold-standard corpora. Recent work has used Wikipedia's link structure to …

Although primarily an encyclopedia, Wikipedia's expansive content provides a knowledge
base that has been continuously exploited by researchers in a wide variety of domains. This …

The aim of educational systems is to assemble learning objects on a set of topics tailored to
the goals and individual students' styles. Given the amount of available Learning Objects …

In this chapter we provide our personal vision of what could be the next generation of Web
search engines, outlining the main research challenges that derive from it. This vision is …

We are currently investigating methods to triplify the content of Wikipedia's tables. We
propose that existing knowledge-bases can be leveraged to semi-automatically extract high …

We study sequential prediction models in cases where only fragments of the sequences are
annotated with the ground-truth. The task does not match the standard semi-supervised …