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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Andreas Niekler , Patrick Jähnichen , Gerhard Heyer

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. More... »

PAGES

812-815

References to SciGraph publications

  • 2002. Introduction to Topic Detection and Tracking in TOPIC DETECTION AND TRACKING
  • Book

    TITLE

    Machine Learning and Knowledge Discovery in Databases

    ISBN

    978-3-642-33485-6
    978-3-642-33486-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-33486-3_53

    DOI

    http://dx.doi.org/10.1007/978-3-642-33486-3_53

    DIMENSIONS

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