Disambiguation of greek polysemous words using hierarchical probabilistic networks and a chi-square feature selection strategy

Fragos, K; Panetsos, S

dc.contributor.author	Fragos, K	en
dc.contributor.author	Panetsos, S	en
dc.date.accessioned	2014-03-01T01:28:10Z
dc.date.available	2014-03-01T01:28:10Z
dc.date.issued	2008	en
dc.identifier.issn	02182130	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18740
dc.subject	Chi-square test	en
dc.subject	Feature selection	en
dc.subject	Hierarchical mixtures-of-experts	en
dc.subject	Word sense disambiguation	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Classifiers	en
dc.subject.other	Computer networks	en
dc.subject.other	Equivalence classes	en
dc.subject.other	Learning systems	en
dc.subject.other	Linguistics	en
dc.subject.other	Probability	en
dc.subject.other	Chi-square test	en
dc.subject.other	Feature selection	en
dc.subject.other	Hierarchical mixtures-of-experts	en
dc.subject.other	Polysemous words	en
dc.subject.other	Word sense disambiguation	en
dc.subject.other	Neural networks	en
dc.title	Disambiguation of greek polysemous words using hierarchical probabilistic networks and a chi-square feature selection strategy	en
heal.type	journalArticle	en
heal.identifier.primary	10.1142/S0218213008004102	en
heal.identifier.secondary	http://dx.doi.org/10.1142/S0218213008004102	en
heal.publicationDate	2008	en
heal.abstract	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. © 2008 World Scientific Publishing Company.	en
heal.journalName	International Journal on Artificial Intelligence Tools	en
dc.identifier.doi	10.1142/S0218213008004102	en
dc.identifier.volume	17	en
dc.identifier.issue	4	en
dc.identifier.spage	687	en
dc.identifier.epage	701	en