▁second ▁language ▁depending ▁on ▁the ▁sense ▁of ▁the ▁word . ▁Word - aligned ▁b iling ual ▁corpor a ▁have ▁been ▁used ▁ ▁to ▁infer ▁cross - ling ual ▁sense ▁dist in ctions , ▁a ▁kind ▁of ▁semi - super vised ▁system . ▁ ▁Un super vised ▁methods ▁ ▁Un super vised ▁learning ▁is ▁the ▁greatest ▁challenge ▁for ▁W SD ▁research ers . ▁The ▁underlying ▁assumption ▁is ▁that ▁similar ▁sens es ▁occur ▁in ▁similar ▁context s , ▁and ▁thus ▁sens es ▁can ▁be ▁induced ▁from ▁text ▁by ▁clust ering ▁word ▁occurr ences ▁using ▁some ▁measure ▁of ▁similarity ▁of ▁context , ▁a ▁task ▁referred ▁to ▁as ▁word ▁sense ▁induction ▁or ▁disc rim ination . ▁Then , ▁new ▁occurr ences ▁of ▁the ▁word ▁can ▁be ▁class ified ▁into ▁the ▁closest ▁induced ▁clusters / s enses . ▁Performance ▁has ▁been ▁lower ▁than ▁for ▁the ▁other ▁methods ▁described ▁above , ▁but ▁compar isons ▁are ▁difficult ▁since ▁sens es ▁induced ▁must ▁be ▁mapped ▁to ▁a ▁known ▁dictionary ▁of ▁word ▁sens es . ▁If ▁a ▁mapping ▁to ▁a ▁set ▁of ▁dictionary ▁sens es ▁is ▁not ▁desired , ▁cluster - based ▁evalu ations ▁( including ▁measures ▁of ▁entropy ▁and ▁pur ity ) ▁can ▁be ▁performed . ▁Alternatively , ▁word ▁sense ▁induction ▁methods ▁can ▁be ▁tested ▁and ▁compared ▁within ▁an ▁application . ▁For ▁instance , ▁it ▁has ▁been ▁shown ▁that ▁word ▁sense ▁induction ▁impro ves ▁Web ▁search ▁result ▁clust ering ▁by ▁increasing ▁the ▁quality ▁of ▁result ▁clusters ▁and ▁the ▁degree ▁divers ification ▁of ▁result ▁lists . ▁It ▁is ▁hoped ▁that ▁un super