ated ▁in ▁the ▁cor pus , ▁and ▁statist ically ▁analyz ing ▁those ▁n ▁surrounding ▁words . ▁Two ▁shall ow ▁approaches ▁used ▁to ▁train ▁and ▁then ▁dis ambigu ate ▁are ▁Na ï ve ▁Bay es ▁class ifiers ▁and ▁decision ▁trees . ▁In ▁recent ▁research , ▁kernel - based ▁methods ▁such ▁as ▁support ▁vector ▁machines ▁have ▁shown ▁superior ▁performance ▁in ▁super vised ▁learning . ▁Graph - based ▁approaches ▁have ▁also ▁gained ▁much ▁attention ▁from ▁the ▁research ▁community , ▁and ▁currently ▁achieve ▁performance ▁close ▁to ▁the ▁state ▁of ▁the ▁art . ▁ ▁Dictionary - ▁and ▁knowledge - based ▁methods ▁The ▁Les k ▁algorithm ▁is ▁the ▁sem inal ▁dictionary - based ▁method . ▁It ▁is ▁based ▁on ▁the ▁hypothesis ▁that ▁words ▁used ▁together ▁in ▁text ▁are ▁related ▁to ▁each ▁other ▁and ▁that ▁the ▁relation ▁can ▁be ▁observed ▁in ▁the ▁definitions ▁of ▁the ▁words ▁and ▁their ▁sens es . ▁Two ▁( or ▁more ) ▁words ▁are ▁dis ambigu ated ▁by ▁finding ▁the ▁pair ▁of ▁dictionary ▁sens es ▁with ▁the ▁greatest ▁word ▁overlap ▁in ▁their ▁dictionary ▁definitions . ▁For ▁example , ▁when ▁dis ambigu ating ▁the ▁words ▁in ▁" pine ▁cone ", ▁the ▁definitions ▁of ▁the ▁appropriate ▁sens es ▁both ▁include ▁the ▁words ▁ever green ▁and ▁tree ▁( at ▁least ▁in ▁one ▁dictionary ). ▁A ▁similar ▁approach ▁searches ▁for ▁the ▁short est ▁path ▁between ▁two ▁words : ▁the ▁second ▁word ▁is ▁iter atively ▁searched ▁among ▁the ▁definitions ▁of ▁every ▁semantic ▁variant ▁of ▁the ▁first ▁word , ▁then ▁among ▁the ▁definitions ▁of ▁every ▁semantic ▁variant ▁of ▁each ▁word ▁in ▁the