Alex Spengler

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Background: The increasing volume and growing complexity of drugs lead to an increased risk of prescription errors and adverse events. A correct drug choice must be modulated to acknowledge both patients' status and drug-specific information. This information is reported in free-text on drug fact sheets. It is often overwhelming and difficult to access.(More)
Web content extraction is concerned with the automatic identification of semantically interesting web page regions. To generalize to pages from unknown sites, it is crucial to exploit not only the local characteristics of a particular web page region, but also the rich interdependencies that exist between the regions and their latent semantics. We therefore(More)
Declaration I declare that this thesis was composed by myself, that the work contained herein is my own except where explicitly stated otherwise in the text, and that this work has not been submitted for any other degree or professional qualification except as specified. Abstract This thesis explores a number of different discriminative models for learning(More)
OBJECTIVE Information about medications is critical in supporting decision-making during the prescription process and thus in improving the safety and quality of care. In this work, we propose a methodology for the automatic recognition of drug-related entities (active ingredient, interaction effects, etc.) in textual drug descriptions, and their further(More)
We consider the problem of content extraction from online news webpages. To explore to what extent the syntactic markup and the visual structure of a webpage facilitate the extraction of its content, we compare two state-of-the-art classifiers as first instantiations of a general framework that allows for proper model comparison. To this end, we introduce(More)
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