期刊名称：Journal of Computer Sciences and Applications
印刷版ISSN：2328-7268
电子版ISSN：2328-725X
出版年度：2013
卷号：1
期号：4
页码：61-74
DOI：10.12691/jcsa-1-4-2
语种：English
出版社：Science and Education Publishing
摘要：In this article, we evaluate features and algorithms for the task of prosodic boundary prediction for Greek. For this purpose a prosodic corpus composed of generic domain text was constructed. Feature contribution was evaluated and ranked with the application of information gain ranking and correlation-based feature selection filtering methods. Resulted datasets were applied to C4.5 decision tree, one-neighbour instance based learner and Bayesian learning methods. Models performance exploitation led as to the construction of a practically optimal feature set whose prediction effectiveness was evaluated with two prosodic databases. In terms of total accuracy and F-measure, evaluation results established the decision tree effectiveness in learning rules for prosodic boundary prediction.
