▁dictionary - based . ▁ ▁In ▁the ▁ 1 9 9 0 s , ▁the ▁statistical ▁revolution ▁swe pt ▁through ▁computational ▁lingu istics , ▁and ▁W SD ▁became ▁a ▁parad ig m ▁problem ▁on ▁which ▁to ▁apply ▁super vised ▁machine ▁learning ▁techniques . ▁ ▁The ▁ 2 0 0 0 s ▁saw ▁super vised ▁techniques ▁reach ▁a ▁plate au ▁in ▁accuracy , ▁and ▁so ▁attention ▁has ▁shift ed ▁to ▁co ar ser - gra ined ▁sens es , ▁domain ▁adaptation , ▁semi - super vised ▁and ▁un super vised ▁cor pus - based ▁systems , ▁combinations ▁of ▁different ▁methods , ▁and ▁the ▁return ▁of ▁knowledge - based ▁systems ▁via ▁graph - based ▁methods . ▁Still , ▁super vised ▁systems ▁continue ▁to ▁perform ▁best . ▁ ▁D iff icult ies ▁ ▁D iffer ences ▁between ▁diction aries ▁One ▁problem ▁with ▁word ▁sense ▁dis ambiguation ▁is ▁dec iding ▁what ▁the ▁sens es ▁are . ▁In ▁cases ▁like ▁the ▁word ▁bass ▁above , ▁at ▁least ▁some ▁sens es ▁are ▁obviously ▁different . ▁In ▁other ▁cases , ▁however , ▁the ▁different ▁sens es ▁can ▁be ▁closely ▁related ▁( one ▁meaning ▁being ▁a ▁met ap hor ical ▁or ▁met onym ic ▁extension ▁of ▁another ), ▁and ▁in ▁such ▁cases ▁division ▁of ▁words ▁into ▁sens es ▁becomes ▁much ▁more ▁difficult . ▁D ifferent ▁diction aries ▁and ▁th es aur uses ▁will ▁provide ▁different ▁divisions ▁of ▁words ▁into ▁sens es . ▁One ▁solution ▁some ▁research ers ▁have ▁used ▁is ▁to ▁choose ▁a ▁particular ▁dictionary , ▁and ▁just ▁use ▁its ▁set