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Titlebook: Artificial Intelligence: Methodology, Systems, and Applications; 17th International C Christo Dichev,Gennady Agre Conference proceedings 20

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,Simulation von Hashtabellen und B*-Bäumen,hension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our d
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Kapitel 6: Praktischer Vorschlag,l knowledge sources such as Wordnets, ontologies, etc. The knowledge encoded in them is typically used as a sense inventory and as a relations bank. However, this type of information is rather sparse in terms of senses and the relations among them. In this paper we present a strategy for the enrichm
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Umgang mit Fehlern auf individueller Ebene,Wikipedia. The translation integrates a tag sense disambiguation algorithm based on WordNet and Wikipedia (as external resources defining word senses). Our disambiguation technique uses the Lesk algorithm, extended gloss overlaps and similarity measures in order to achieve successful resolution of l
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https://doi.org/10.1007/978-3-658-05293-5in industries to be able to determine how much of that news is actually being read, which news items are not interest generating or, indeed, if there are topics being discussed on Twitter that have not even been reported in the news. Twitter generates vast numbers of Tweets daily and has a massive a
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Umgang mit Fehlern auf individueller Ebene,hich benefit from them during development process. Since internet has become a part of our daily lives, the number of reviews expands; it is getting difficult day by day to obtain a comprehensive view of user opinions from these reviews manually. Thus, sentiment analysis becomes an indispensable tas
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https://doi.org/10.1007/978-3-319-44748-3ant colony optimization; data mining; intelligent agents; recommender systems; soft computing; anomaly de
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