@article {IOPORT.06078217,
    author       = {Hristea, Florentina and Colhon, Mihaela},
    title        = {Feeding syntactic versus semantic knowledge to a knowledge-lean unsupervised word sense disambiguation algorithm with an underlying Na{\"\i}ve Bayes model.},
    year         = {2012},
    journal      = {Fundamenta Informaticae},
    volume       = {119},
    number       = {1},
    issn         = {0169-2968},
    pages        = {61-86},
    publisher    = {Polish Mathematical Society, Warsaw; IOS Press, Amsterdam},
    abstract     = {Summary: The present paper concentrates on the issue of feature selection for unsupervised word sense disambiguation (WSD) performed with an underlying Na{\"\i}ve Bayes model. It introduces dependency-based feature selection which, to our knowledge, is used for the first time in conjunction with the Na{\"\i}ve Bayes model acting as clustering technique. Construction of the dependency-based semantic space required for the proposed task is discussed. The resulting disambiguation method, representing an extension of the method introduced in [15], lies at the border between unsupervised and knowledge-based techniques. Syntactic knowledge provided by dependency relations (and exemplified in the case of adjectives) is hereby compared to semantic knowledge offered by the semantic network WordNet (and examined in [15]). Our conclusion is that the Na{\"\i}ve Bayes model reacts well in the presence of syntactic knowledge of this type and that dependency-based feature selection is a reliable alternative to the WordNet-based semantic one.},
    identifier   = {06078217},
}