Aron Schmidt

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In this paper, we explore the idea of building an adaptive user interest model. Our proposed system uses implicit data extracted from a user’s search queries to select categorical information from DBpedia. By combining the categorical information collected from multiple queries and exploiting the semantic relationships between these categories, it becomes(More)
This paper presents a non-traditional “Anti-Bayesian” solution for the traditional Text Classification (TC) problem. Historically, all the recorded TC schemes work using the fundamental paradigm that once the statistical features are inferred from the syntactic/semantic indicators, the classifiers themselves are the well-established statistical ones. In(More)
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