▁rely ▁on ▁knowledge ▁about ▁word ▁sens es , ▁which ▁is ▁only ▁sp ars ely ▁form ulated ▁in ▁diction aries ▁and ▁lex ical ▁databases . ▁Super vised ▁methods ▁depend ▁cru cially ▁on ▁the ▁existence ▁of ▁manually ▁annot ated ▁examples ▁for ▁every ▁word ▁sense , ▁a ▁requ is ite ▁that ▁can ▁so ▁far ▁be ▁met ▁only ▁for ▁a ▁hand ful ▁of ▁words ▁for ▁testing ▁purposes , ▁as ▁it ▁is ▁done ▁in ▁the ▁S ense val ▁exer cis es . ▁ ▁One ▁of ▁the ▁most ▁prom ising ▁tr ends ▁in ▁W SD ▁research ▁is ▁using ▁the ▁largest ▁cor pus ▁ever ▁accessible , ▁the ▁World ▁W ide ▁Web , ▁to ▁ac quire ▁lex ical ▁information ▁automatically . ▁W SD ▁has ▁been ▁tradition ally ▁understood ▁as ▁an ▁intermediate ▁language ▁engineering ▁technology ▁which ▁could ▁improve ▁applications ▁such ▁as ▁information ▁retr ieval ▁( IR ). ▁In ▁this ▁case , ▁however , ▁the ▁reverse ▁is ▁also ▁true : ▁web ▁search ▁engines ▁implement ▁simple ▁and ▁robust ▁IR ▁techniques ▁that ▁can ▁successfully ▁mine ▁the ▁Web ▁for ▁information ▁to ▁use ▁in ▁W SD . ▁The ▁historic ▁lack ▁of ▁training ▁data ▁has ▁prov oked ▁the ▁appearance ▁of ▁some ▁new ▁algorithms ▁and ▁techniques , ▁as ▁described ▁in ▁Autom atic ▁ac quisition ▁of ▁sense - tag ged ▁corpor a . ▁ ▁External ▁knowledge ▁sources ▁Know ledge ▁is ▁a ▁fundamental ▁component ▁of ▁W SD . ▁Know ledge ▁sources ▁provide ▁data ▁which ▁are ▁essential ▁to ▁associate ▁sens es ▁with ▁words . ▁They ▁can ▁vary ▁from ▁corpor a ▁of ▁texts , ▁either ▁un l abeled ▁or ▁annot ated ▁with ▁word