A Word Sense Disambiguation System Using Modified Naive Bayesian Algorithms for Indonesian Language
Kazuya Shitaoka, Kiyotaka Uchimoto, Tatsuya Kawahara, Hitoshi Isahara · Journal of Natural Language Processing · 2005
It is common that a word in any natural language has often more than one meaning/sense. A word sense disambiguation (WSD) system is designed to determine which one of the senses of a polysemous word is invoked in a particular context around the word. We propose methods to disambiguate senses of polysemous words by using Naive Bayesian classifier method. A few sets of experiment data were taken from Kompas daily newspaper homepage and used for the system construction. We modified the original algorithm of Naive Bayesian method to apply it to the Indonesian language analysis. The experiments showed that our system achieved good accuracies (73-99%).