, ▁using ▁a ▁training ▁cor pus ▁of ▁words ▁tag ged ▁with ▁their ▁word ▁sens es . ▁This ▁approach , ▁while ▁theoret ically ▁not ▁as ▁powerful ▁as ▁deep ▁approaches , ▁gives ▁superior ▁results ▁in ▁practice , ▁due ▁to ▁the ▁computer ' s ▁limited ▁world ▁knowledge . ▁However , ▁it ▁can ▁be ▁confused ▁by ▁sentences ▁like ▁The ▁dogs ▁b ark ▁at ▁the ▁tree ▁which ▁contains ▁the ▁word ▁b ark ▁near ▁both ▁ ▁tree ▁and ▁dogs . ▁ ▁There ▁are ▁four ▁conventional ▁approaches ▁to ▁W SD : ▁ ▁Dictionary - ▁and ▁knowledge - based ▁methods : ▁These ▁rely ▁primarily ▁on ▁diction aries , ▁th esa uri , ▁and ▁lex ical ▁knowledge ▁bases , ▁without ▁using ▁any ▁cor pus ▁evidence . ▁ ▁Sem i - super vised ▁or ▁minim ally ▁super vised ▁methods : ▁These ▁make ▁use ▁of ▁a ▁secondary ▁source ▁of ▁knowledge ▁such ▁as ▁a ▁small ▁annot ated ▁cor pus ▁as ▁seed ▁data ▁in ▁a ▁boot stra pping ▁process , ▁or ▁a ▁word - aligned ▁b iling ual ▁cor pus . ▁ ▁Super vised ▁methods : ▁These ▁make ▁use ▁of ▁sense - annot ated ▁corpor a ▁to ▁train ▁from . ▁ ▁Un super vised ▁methods : ▁These ▁es ch ew ▁( al most ) ▁completely ▁external ▁information ▁and ▁work ▁directly ▁from ▁raw ▁un annot ated ▁corpor a . ▁These ▁methods ▁are ▁also ▁known ▁under ▁the ▁name ▁of ▁word ▁sense ▁disc rim ination . ▁ ▁Al most ▁all ▁these ▁approaches ▁normally ▁work ▁by ▁defining ▁a ▁window ▁of ▁n ▁content ▁words ▁around ▁each ▁word ▁to ▁be ▁dis ambigu