COPYRIGHT YEAR

2012

AUTHORS

Patrick Jähnichen, Andreas Niekler, Gerhard Heyer

TITLE

ASV Monitor: Creating Comparability of Machine Learning Methods for Content Analysis

ABSTRACT

In this demonstration paper we present an application to compare and evaluate machine learning methods used for natural language processing within a content analysis framework. Our aim is to provide an example set of possible machine learning results for different inputs to increase the acceptance of using machine learning in settings that originally rely on manual treatment. We will demonstrate the possibility to compare machine learning algorithms regarding the outcome of the implemented approaches. The application allows the user to evaluate the benefit of using machine learning algorithms for content analysis by a visual comparison of their results.

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28 TRIPLES      25 PREDICATES      25 URIs      13 LITERALS

Subject Predicate Object
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7 sg:copyrightYear 2012
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9 sg:doi 10.1007/978-3-642-33486-3_53
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18 sg:language En
19 sg:license http://scigraph.springernature.com/explorer/license/
20 sg:metadataRights OpenAccess
21 sg:pageFirst 812
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23 sg:scigraphId e84ba04e1b59b1198b2137be4a5d9d74
24 sg:title ASV Monitor: Creating Comparability of Machine Learning Methods for Content Analysis
25 sg:webpage https://link.springer.com/10.1007/978-3-642-33486-3_53
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28 owl:sameAs http://lod.springer.com/data/bookchapter/978-3-642-33486-3_53
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N-Triples is a line-based linked data format ideal for batch operations .

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