Unsupervised learning is the greatest challenge for WSD researchers. The underlying assumption is that similar senses occur in similar contexts, and thus senses can be induced from text by clustering word occurrences using some measure of similarity of context, a task referred to as word sense induction or discrimination. Then, new occurrences of the word can be classified into the closest induced clusters/senses. Performance has been lower than for the other methods described above, but comparisons are difficult since senses induced must be mapped to a known dictionary of word senses. If a mapping to a set of dictionary senses is not desired, cluster-based evaluations (including measures of entropy and purity) can be performed. Alternatively, word sense induction methods can be tested and compared within an application. For instance, it has been shown that word sense induction improves Web search result clustering by increasing the quality of result clusters and the degree diversification of result lists. It is hoped that unsupervised learning will overcome the knowledge acquisition bottleneck because they are not dependent on manual effort.
UNSuPervised learniNg is ThE GREaTeSt ChalLEnGe for wsD researcHeRs. the underlYiNg AssumpTiOn iS THaT similaR seNseS ocCur in simILar cOntExts, aND thus sEnses can be iNduceD from text bY clustERIng woRD OCcUrrenCes UsiNg somE meAsuRe Of simiLaRitY oF coNtEXt, A tasK REfErreD to As wOrD sense InducTIon or dIscrImINaTiON. THEn, neW oCcURREnceS of thE woRd cAN Be cLAssifieD intO tHe cLoSEST Induced clUsters/sEnseS. PerformANCe has bEen lower than fOr The otHer methOdS desCRIbed ABove, buT comPariSonS are diFfiCulT sInce SENses InducEd musT Be mapped TO a kNowN dIctionarY oF wOrd SenSEs. If A maPpiNg to a SeT of dicTiONary seNSes Is Not desIred, clUstER-bAsEd EVAluatIons (inclUding mEasureS of eNtRopy and puritY) cAn be peRFormed alternAtIvely, Word SeNSe inDUCTion MethoDs can be tested and ComPared wItHiN aN ApplicatioN. FOr inStance, It Has BeeN showN ThAt word Sense inductION Improves wEb SEaRCh resULt clUsTerIng By increaSiNg THE qUalITy oF resuLT cLUsters and the DEgree DivERsIficatiON OF result LISTs. it Is hOPeD tHaT unsUpErvised LEarNing WilL overcome the Knowledge AcQuisItIon BottlenecK because They aRE noT dEpendent on mAnual EFfoRt.