Slobodan Beliga

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Our approach proposes a novel network measure-the node selectivity for the task of keyword extraction. The node selectivity is defined as the average strength of the node. Firstly, we show that selectivity-based keyword extraction slightly outperforms the extraction based on the standard centrality measures: in-degree, out-degree, betweenness, and(More)
This paper presents text normalization which is an integral part of any text-to-speech synthesis system. Text normalization is a set of methods with a task to write non-standard words, like numbers, dates, times, abbreviations, acronyms and the most common symbols, in their full expanded form are presented. The whole taxonomy for classification of(More)
This paper presents categorization of Croatian texts using Non-Standard Words (NSW) as features. Non-Forest algorithms were used in text categorization experiments. The best categorization results are achieved using the first feature set (NSW frequencies) with the categorization accuracy of 87%. This suggests that the NSWs should be considered as features(More)
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