es . ▁Among ▁these , ▁super vised ▁learning ▁approaches ▁have ▁been ▁the ▁most ▁successful ▁algorithms ▁to ▁date . ▁ ▁Acc ur acy ▁of ▁current ▁algorithms ▁is ▁difficult ▁to ▁state ▁without ▁a ▁host ▁of ▁cave ats . ▁In ▁English , ▁accuracy ▁at ▁the ▁co arse - gra ined ▁( hom ograph ) ▁level ▁is ▁rout in ely ▁above ▁ 9 0 %, ▁with ▁some ▁methods ▁on ▁particular ▁hom ograph s ▁ach ieving ▁over ▁ 9 6 %. ▁On ▁fin er - gra ined ▁sense ▁dist in ctions , ▁top ▁acc ura cies ▁from ▁ 5 9 . 1 % ▁to ▁ 6 9 . 0 % ▁have ▁been ▁reported ▁in ▁evaluation ▁exer cis es ▁( Sem E val - 2 0 0 7 , ▁S ense val - 2 ), ▁where ▁the ▁bas eline ▁accuracy ▁of ▁the ▁simplest ▁possible ▁algorithm ▁of ▁always ▁choosing ▁the ▁most ▁frequent ▁sense ▁was ▁ 5 1 . 4 % ▁and ▁ 5 7 %, ▁respectively . ▁ ▁About ▁Dis ambiguation ▁requires ▁two ▁strict ▁inputs : ▁a ▁dictionary ▁to ▁specify ▁the ▁sens es ▁which ▁are ▁to ▁be ▁dis ambigu ated ▁and ▁a ▁cor pus ▁of ▁language ▁data ▁to ▁be ▁dis ambigu ated ▁( in ▁some ▁methods , ▁a ▁training ▁cor pus ▁of ▁language ▁examples ▁is ▁also ▁required ). ▁W SD ▁task ▁has ▁two ▁variants : ▁" lex ical ▁sample " ▁and ▁" all ▁words " ▁task . ▁The ▁former ▁compr ises ▁dis ambigu ating ▁the ▁occurr ences ▁of ▁a ▁small ▁sample ▁of ▁target ▁words ▁which ▁were ▁previously ▁selected , ▁while