Almost all these approaches work by defining a window of n content words around each word to be disambiguated in the corpus, and statistically analyzing those n surrounding words. Two shallow approaches used to train and then disambiguate are Naïve Bayes classifiers and decision trees. In recent research, kernel-based methods such as support vector machines have shown superior performance in supervised learning. Graph-based approaches have also gained much attention from the research community, and currently achieve performance close to the state of the art.
alMoSt all thesE appRoAchEs Work bY Defining a wINDow of n ConTEnT worDs Around each wOrd to be disAmBiGuATed in the CORPUs, aNd sTAtisticallY A,nalYzIng tHoSe n sURRoUNdIng wor,ds. tWo shallOw approaches used to trAin aND then dIsAMbiGuAte Are naïve baYes Cla.ssifiers aND decIsI:on treeS. in ReC,enT reSearCh, KernEl-based meThoDs such aS sUPpOrt vectoR MachIneS HavE sHown supErIor peRFormAnce in suPErvIsEd LearninG. graph-bAsed aPprOaches HAve aLso GAINed much atteNtiOn from tHe researcH coMMUnity, anD CurreNtly AcHieve peRfoRmAnce clOse to tHe stAte of tHe aRt.