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. NonStandard Words are: numbers, dates, acronyms, abbreviations, currency, etc. NSWs in Croatian language are determined according to Croatian NSW taxonomy. For the purpose of this research, 390 text documents were collected and formed the SKIPEZ collection with(More)
  • Shruti Luthra, Dinkar Arora, +8 authors Anu H Nair
  • 2017
Large number of techniques for keyword extraction have been proposed for better matching of documents with the user’s query but most of them deal with tf-idf to find the weight age of query terms in the entire document but this can result in improper result as if a term has a low term frequency in overall document but high frequency in a certain part of the(More)
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