ourse ’ ▁properties ▁of ▁human ▁languages ▁for ▁word ▁sense ▁dis ambiguation . ▁From ▁observation , ▁words ▁tend ▁to ▁exhib it ▁only ▁one ▁sense ▁in ▁most ▁given ▁disc ourse ▁and ▁in ▁a ▁given ▁col location . ▁ ▁The ▁boot stra pping ▁approach ▁starts ▁from ▁a ▁small ▁amount ▁of ▁seed ▁data ▁for ▁each ▁word : ▁either ▁manually ▁tag ged ▁training ▁examples ▁or ▁a ▁small ▁number ▁of ▁su ref ire ▁decision ▁rules ▁( e . g ., ▁' play ' ▁in ▁the ▁context ▁of ▁' b ass ' ▁almost ▁always ▁indicates ▁the ▁musical ▁instrument ). ▁The ▁se eds ▁are ▁used ▁to ▁train ▁an ▁initial ▁class ifier , ▁using ▁any ▁super vised ▁method . ▁This ▁class ifier ▁is ▁then ▁used ▁on ▁the ▁un tag ged ▁portion ▁of ▁the ▁cor pus ▁to ▁extract ▁a ▁larger ▁training ▁set , ▁in ▁which ▁only ▁the ▁most ▁confident ▁class ifications ▁are ▁included . ▁The ▁process ▁repe ats , ▁each ▁new ▁class ifier ▁being ▁trained ▁on ▁a ▁success ively ▁larger ▁training ▁cor pus , ▁until ▁the ▁whole ▁cor pus ▁is ▁consum ed , ▁or ▁until ▁a ▁given ▁maximum ▁number ▁of ▁iterations ▁is ▁reached . ▁ ▁Other ▁semi - super vised ▁techniques ▁use ▁large ▁quantities ▁of ▁un tag ged ▁corpor a ▁to ▁provide ▁co - occ urrence ▁information ▁that ▁supp lement s ▁the ▁tag ged ▁corpor a . ▁These ▁techniques ▁have ▁the ▁potential ▁to ▁help ▁in ▁the ▁adaptation ▁of ▁super vised ▁models ▁to ▁different ▁domains . ▁ ▁Also , ▁an ▁ambigu ous ▁word ▁in ▁one ▁language ▁is ▁often ▁translated ▁into ▁different ▁words ▁in ▁a