Yannis Papanikolaou

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This paper documents the systems that we developed for our participation in the BioASQ 2014 large-scale bio-medical semantic indexing and question answering challenge. For the large-scale semantic indexing task, we employed a novel multi-label ensemble method consisting of support vector machines, labeled Latent Dirichlet Allocation models and meta-models(More)
In this paper we present the methods and the approaches employed in terms of our participation to the BioASQ Challenge 2015 and more specifically in task 3a, concerning the automatic semantic annotation of scientific abstracts. Based on the successful approaches of the previous years we considered a variety of ensembles, incorporated journal-specific(More)
In this paper we present the methods and approaches employed in terms of our participation in the 2016 version of the BioASQ challenge. For the semantic indexing task, we extended our successful ensemble approach of last year with additional models. The official results obtained so-far demonstrate a continuing consistent advantage of our approaches against(More)
—Hierarchy Of Multi-label classifiERs (HOMER) is a multi-label learning algorithm that breaks the initial learning task to several, easier sub-tasks by first constructing a hierarchy of labels from a given label set and secondly employing a given base multi-label classifier (MLC) to the resulting sub-problems. The primary goal is to effectively address(More)
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