Akyokuş, Selim (Dogus Author) -- Ganiz, Murat C. (Dogus Author) -- Conference full title: 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2011) Istanbul, Turkey, 15 - 18 June 2011A web crawler is defined as an automated program that methodically scans through Internet pages and downloads any page that can be reached via links. With the exponential growth of the Web, fetching information about a special-topic is gaining importance. A focused crawler is a web crawler that attempts to download only web pages that are relevant to a predefined topic or set of topics. In order to determine a web page is about a particular topic, focused crawlers use classification techniques. In this study we focus on the classification of links instead of downloaded web pages to determine relevancy. We combine a Naïve Bayes classifier for classification of URLs with a simple URL scoring optimization to improve the system performance. Our results demonstrate that proposed approach performs better.TUBITAK, IEEE
Ganiz, Murat Can (Dogus Author), Akyokuş, Selim (Dogus Author) -- Full conference title: INISTA 2012: International Symposium on Innovations in Intelligent Systems and Applications: 2-4 July, 2012: Trabzon, TurkeyMajority of the existing text classification algorithms are based on the "bag of words" (BOW) approach, in which the documents are represented as weighted occurrence frequencies of individual terms. However, semantic relations between terms are ignored in this representation. There are several studies which address this problem by integrating background knowledge such as WordNet, ODP or Wikipedia as a semantic source. However, vast majority of these studies are applied to English texts and to the date there are no similar studies on classification of Turkish documents. We empirically analyze the effect of using Turkish Wikipedia (Vikipedi) as a semantic resource in classification of Turkish documents. Our results demonstrate that performance of classification algorithms can be improved by exploiting Vikipedi concepts. Additionally, we show that Vikipedi concepts have surprisingly large coverage in our datasets which mostly consist of Turkish newspaper articles
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