2009. Enhancement of lexical concepts using cross-lingual web mining

Sets of lexical items sharing a significant aspect of their meaning (concepts) are fun-damental in linguistics and NLP. Manual concept compilation is labor intensive, er-ror prone and subjective. We present a web-based concept extension algorithm. Given a set of terms specifying a concept in some language, we translate them to a wide range of intermediate languages, disambiguate the translations using web counts, and discover additional concept terms using symmetric patterns. We then translate the discovered terms back into the original language, score them, and ex-tend the original concept by adding back-translations having high scores. We eval-uate our method in 3 source languages and 45 intermediate languages, using both hu-man judgments and WordNet. In all cases, our cross-lingual algorithm significantly improves high quality concept extension.