  Linking concepts and named entities to knowledge bases has become a crucial
Natural Language Understanding task. In this respect, recent works have shown
the key advantage of exploiting textual definitions in various Natural Language
Processing applications. However, to date there are no reliable large-scale
corpora of sense-annotated textual definitions available to the research
community. In this paper we present a large-scale high-quality corpus of
disambiguated glosses in multiple languages, comprising sense annotations of
both concepts and named entities from a unified sense inventory. Our approach
for the construction and disambiguation of the corpus builds upon the structure
of a large multilingual semantic network and a state-of-the-art disambiguation
system; first, we gather complementary information of equivalent definitions
across different languages to provide context for disambiguation, and then we
combine it with a semantic similarity-based refinement. As a result we obtain a
multilingual corpus of textual definitions featuring over 38 million
definitions in 263 languages, and we make it freely available at
http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information
Extraction and Sense Clustering show how two state-of-the-art approaches
improve their performance by integrating our disambiguated corpus into their
pipeline.
