DISAMBIGUATION OF GREEK POLYSEMOUS WORDS USING HIERARCHICAL PROBABILISTIC NETWORKS AND A CHI-SQUARE FEATURE SELECTION STRATEGY.

In this paper, we present a robust classification technique to disambiguate Greek polysemous words based on hierarchical probabilistic networks. Assuming that the linguistic data of a polysemous word is classified to equivalent classes according to the number of its senses, we try to disambiguate them by using a hierarchical mixture of experts' probabilistic model — a soft version of neural networks that permits overlapping between classes and which is successfully applied in classification tasks. The model is used in combination with an effective feature selection strategy based on a Chi-square test that enhances disambiguation performance. Due to the absence of previous similar work on Greek linguistic data, for comparison we also implement and apply the very popular naïve Bayes classifier to the same data. Comparing the two systems, we find that the Hierarchical Mixtures-of-Experts (HME) model is superior to the naïve Bayes classifier, mainly because of its ability to permit overlapping and the capture of non-linearity among the data. We believe that the system can be successfully applied on real linguistic data after training on a small amount of data. [ABSTRACT FROM AUTHOR]