vised ▁learning ▁will ▁overcome ▁the ▁knowledge ▁ac quisition ▁bott l ene ck ▁because ▁they ▁are ▁not ▁dependent ▁on ▁manual ▁effort . ▁ ▁Represent ing ▁words ▁considering ▁their ▁context ▁through ▁fixed ▁size ▁dense ▁vectors ▁( word ▁embed d ings ) ▁has ▁become ▁one ▁of ▁the ▁most ▁fundamental ▁blocks ▁in ▁several ▁N LP ▁systems . ▁Even ▁though ▁most ▁of ▁traditional ▁word ▁embedding ▁techniques ▁confl ate ▁words ▁with ▁multiple ▁mean ings ▁into ▁a ▁single ▁vector ▁representation , ▁they ▁still ▁can ▁be ▁used ▁to ▁improve ▁W SD . ▁In ▁addition ▁to ▁word ▁embed d ings ▁techniques , ▁lex ical ▁databases ▁( e . g ., ▁Word Net , ▁Con cept Net , ▁B abel Net ) ▁can ▁also ▁assist ▁un super vised ▁systems ▁in ▁mapping ▁words ▁and ▁their ▁sens es ▁as ▁diction aries . ▁Some ▁techniques ▁that ▁combine ▁lex ical ▁databases ▁and ▁word ▁embed d ings ▁are ▁presented ▁in ▁Auto Ext end ▁and ▁Most ▁Su itable ▁S ense ▁An notation ▁( M SS A ). ▁In ▁Auto Ext end , ▁they ▁present ▁a ▁method ▁that ▁dec ou ples ▁an ▁object ▁input ▁representation ▁into ▁its ▁properties , ▁such ▁as ▁words ▁and ▁their ▁word ▁sens es . ▁Auto Ext end ▁uses ▁a ▁graph ▁structure ▁to ▁map ▁words ▁( e . g . ▁text ) ▁and ▁non - word ▁( e . g . ▁syn sets ▁in ▁Word Net ) ▁objects ▁as ▁nodes ▁and ▁the ▁relationship ▁between ▁nodes ▁as ▁edges . ▁The ▁relations ▁( ed ges ) ▁in ▁Auto Ext end ▁can ▁either ▁express ▁the ▁addition ▁or ▁similarity ▁between ▁its