▁one ▁can ▁dis ambigu ate ▁the ▁word ▁bass ▁in ▁" I ▁am ▁cook ing ▁bass es " ▁( i . e ., ▁it ' s ▁not ▁a ▁musical ▁instrument ). ▁ ▁Super vised ▁methods ▁Super vised ▁methods ▁are ▁based ▁on ▁the ▁assumption ▁that ▁the ▁context ▁can ▁provide ▁enough ▁evidence ▁on ▁its ▁own ▁to ▁dis ambigu ate ▁words ▁( h ence , ▁common ▁sense ▁and ▁reasoning ▁are ▁de emed ▁unnecessary ). ▁Probably ▁every ▁machine ▁learning ▁algorithm ▁going ▁has ▁been ▁applied ▁to ▁W SD , ▁including ▁associated ▁techniques ▁such ▁as ▁feature ▁selection , ▁parameter ▁optimization , ▁and ▁ensemble ▁learning . ▁Support ▁Vector ▁Mach ines ▁and ▁memory - based ▁learning ▁have ▁been ▁shown ▁to ▁be ▁the ▁most ▁successful ▁approaches , ▁to ▁date , ▁probably ▁because ▁they ▁can ▁co pe ▁with ▁the ▁high - dimensional ity ▁of ▁the ▁feature ▁space . ▁However , ▁these ▁super vised ▁methods ▁are ▁subject ▁to ▁a ▁new ▁knowledge ▁ac quisition ▁bott l ene ck ▁since ▁they ▁rely ▁on ▁substantial ▁amounts ▁of ▁manually ▁sense - tag ged ▁corpor a ▁for ▁training , ▁which ▁are ▁labor ious ▁and ▁expensive ▁to ▁create . ▁ ▁Sem i - super vised ▁methods ▁Because ▁of ▁the ▁lack ▁of ▁training ▁data , ▁many ▁word ▁sense ▁dis ambiguation ▁algorithms ▁use ▁semi - super vised ▁learning , ▁which ▁allows ▁both ▁l abeled ▁and ▁un l abeled ▁data . ▁ ▁The ▁Y ar ow sky ▁algorithm ▁was ▁an ▁early ▁example ▁of ▁such ▁an ▁algorithm . ▁It ▁uses ▁the ▁‘ One ▁sense ▁per ▁col location ’ ▁and ▁the ▁‘ One ▁sense ▁per ▁disc