▁nodes . ▁The ▁former ▁capt ures ▁the ▁intu ition ▁behind ▁the ▁offset ▁calculus , ▁while ▁the ▁latter ▁defines ▁the ▁similarity ▁between ▁two ▁nodes . ▁In ▁M SS A , ▁an ▁un super vised ▁dis ambiguation ▁system ▁uses ▁the ▁similarity ▁between ▁word ▁sens es ▁in ▁a ▁fixed ▁context ▁window ▁to ▁select ▁the ▁most ▁suitable ▁word ▁sense ▁using ▁a ▁pre - tra ined ▁word ▁embedding ▁model ▁and ▁Word Net . ▁For ▁each ▁context ▁window , ▁M SS A ▁calcul ates ▁the ▁cent roid ▁of ▁each ▁word ▁sense ▁definition ▁by ▁aver aging ▁the ▁word ▁vectors ▁of ▁its ▁words ▁in ▁Word Net ' s ▁gl oss es ▁( i . e ., ▁short ▁defining ▁gl oss ▁and ▁one ▁or ▁more ▁usage ▁example ) ▁using ▁a ▁pre - tra ined ▁word ▁embed d ings ▁model . ▁These ▁cent roid s ▁are ▁later ▁used ▁to ▁select ▁the ▁word ▁sense ▁with ▁the ▁highest ▁similarity ▁of ▁a ▁target ▁word ▁to ▁its ▁immediately ▁adjacent ▁neighb ors ▁( i . e ., ▁prede cess or ▁and ▁successor ▁words ). ▁After ▁all ▁words ▁are ▁annot ated ▁and ▁dis ambigu ated , ▁they ▁can ▁be ▁used ▁as ▁a ▁training ▁cor pus ▁in ▁any ▁standard ▁word ▁embedding ▁technique . ▁In ▁its ▁improved ▁version , ▁M SS A ▁can ▁make ▁use ▁of ▁word ▁sense ▁embed d ings ▁to ▁repeat ▁its ▁dis ambiguation ▁process ▁iter atively . ▁ ▁Other ▁approaches ▁Other ▁approaches ▁may ▁vary ▁differently ▁in ▁their ▁methods : ▁ ▁Dis ambiguation ▁based ▁on ▁oper ational ▁semantics ▁of ▁default ▁logic . ▁ ▁Domain - dri ven ▁dis ambiguation