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RDRCE: Combining Machine Learning and Knowledge Acquisition

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Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6232))

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Abstract

We present a new interactive workbench RDRCE (RDR Case Explorer) to facilitate the combination of Machine Learning and manual Knowledge Acquisition for Natural Language Processing problems. We show how to use Brill’s well regarded transformational learning approach and convert its results into an RDR tree. RDRCE then strongly guides the systematic inspection of the generated RDR tree in order to further refine and improve it by manually adding more rules. Furthermore, RDRCE also helps in quickly recognising potential noise in the training data and allows to deal with noise effectively. Finally, we present a first study using RDRCE to build a high-quality Part-of-Speech tagger for English. After some 60 hours of manual knowledge acquisition, we already exceed slightly the state-of-the art performance on unseen benchmark test data and the fruits of some 15 years of further research in learning methods for Part-of-Speech taggers.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Han Xu & Achim Hoffmann

Authors
  1. Han Xu
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  2. Achim Hoffmann
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Editor information

Editors and Affiliations

  1. School of Computing ad Information Systems, University of Tasmania, TAS7250, Launceton, Tasmania, Australia

    Byeong-Ho Kang

  2. Computing Department,Division of Information and Communication Sciences, Macquarie University, 2109, Sydney, NSW, Australia

    Debbie Richards

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Xu, H., Hoffmann, A. (2010). RDRCE: Combining Machine Learning and Knowledge Acquisition. In: Kang, BH., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2010. Lecture Notes in Computer Science(), vol 6232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15037-1_15

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  • DOI: https://doi.org/10.1007/978-3-642-15037-1_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15036-4

  • Online ISBN: 978-3-642-15037-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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