Ontology population using corpus statistics

This paper presents a combination of algorithms for automatic ontology building based mainly on lexical cooccurrence statistics. We populate an ontology with hypernymy links, thus we refer more specifically to a taxonomy of lexical units (nouns organized by hypernymy relations) rather than an ontology of formally defined concepts. A set of combined statistical procedures produce fragments of taxonomies from corpora that are later integrated into a unified taxonomy by a central algorithm. Our results show that with an ensemble of different components it is possible to achieve an accuracy only slightly worse than human performance. Finally, as our methods are based on quantitative linguistics, the algorithm we propose is not language specific. The language used for the experiments is, however, Spanish.