Global ETD Search

11	Designing Random Sample Synopses with Outliers Lehner, Wolfgang, Rosch, Philip, Gemulla, Rainer 12 August 2022 (has links) Random sampling is one of the most widely used means to build synopses of large datasets because random samples can be used for a wide range of analytical tasks. Unfortunately, the quality of the estimates derived from a sample is negatively affected by the presence of 'outliers' in the data. In this paper, we show how to circumvent this shortcoming by constructing outlier-aware sample synopses. Our approach extends the well-known outlier indexing scheme to multiple aggregation columns. info:eu-repo/classification/ddc/004 ddc:004
12	An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition Tsatsaronis, George 10 October 2017 (has links) (PDF) This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. BioASQ-Wettbewerb hierarchische Textsystematik semantische Indizierung Informationsbeschaffung Passage Retrieval Fragebeantwortung Mehrfachdokumentation Technsiche Universität Dresden Publikationsfonds BioASQ Competition Hierarchical Text Classification Semantic indexing Information retrieval Passage retrieval Question answering Multi-document text summarization Technische Universität Dresden Publishing Fund ddc:570 rvk:WA 15000
13	An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition Tsatsaronis, George 10 October 2017 (has links) This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. info:eu-repo/classification/ddc/570 ddc:570

Page generated in 0.0613 seconds