ASV monitor: creating comparability of machine learning methods for content analysis

Authors:
Andreas Niekler;Patrick Jähnichen;Gerhard Heyer
Affiliations:
Faculty of Media, Leipzig University of Applied Sciences (HTWK), Germany;NLP Group, Department of Computer Science, University of Leipzig, Germany;NLP Group, Department of Computer Science, University of Leipzig, Germany
Venue:
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Year:
2012

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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.