Babelplagiarism: What can BabelNet do for
      Cross-language Plagiarism Detection?

                                   Roberto Navigli

                           Univeristy La Sapienza, Rome, Italy
                                 navigli@di.uniroma1.it

In the first part of the talk, I will present BabelNet, a very large, wide-coverage
multilingual semantic network. The resource is automatically constructed by means of
a methodology that integrates lexicographic and encyclopedic knowledge from
WordNet and Wikipedia. In addition Machine Translation is also applied to enrich the
knowledge resource with lexical information for all languages. We present
experiments on new and existing gold-standard datasets to show the high quality and
coverage of the resource. In a second set of experiments, we show that, when
provided with a vast amount of high-quality semantic relations, knowledge-rich word
sense disambiguation algorithms compete with state-of-the-art supervised WSD
systems in a coarse-grained all-words setting and outperform them on gold-standard
domain-specific datasets.
The second part of the talk is devoted to analyzing cases in which BabelNet can be of
help in cross-language plagiarism detection. Can a large multilingual semantic
network provide hints for detecting plagiarized text? We will see examples of how
and when multilingual concepts and disambiguated text can support this task.