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Two-Tier Machine Learning Using Conditional Random Fields with Constraints

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 454))

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Abstract

This paper shows a novel approach of two-tier machine learning to locate bibliographic references in HTML and separate them into fields. First it is demonstrated, how Conditional Random Fields (CRFs) with constraints can be used to split bibliographic references into fields e.g. authors and title. Therefore a unique feature set, constraints and a method for automatic keyword extraction are introduced. The output of this CRF for tagging bibliographic references, Part Of Speech (POS) analysis and Named Entity Recognition (NER) build the first tier and their output is used to locate the bibliographic reference section in the first place. For this the documents are split into blocks, which are then used for classification. For this task a Support Vector Machines (SVM) approach is compared with another one using a CRF. We demonstrate this two-tier approach archives very good results, while the reference tagging approach is able to compete with other state-of-the-art approaches.

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Notes

  1. 1.

    http://scholar.google.com.

  2. 2.

    http://citeseerx.ist.psu.edu/index.

  3. 3.

    http://www.springermaterials.com.

  4. 4.

    http://www.springerreference.com.

  5. 5.

    http://jsoup.org/.

  6. 6.

    seleniumhq.org.

  7. 7.

    http://aye.comp.nus.edu.sg/parsCit/.

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Author information

Authors and Affiliations

  1. illucIT Software GmbH, Würzburg, Germany

    Sebastian Lindner

Authors
  1. Sebastian Lindner
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Correspondence to Sebastian Lindner .

Editor information

Editors and Affiliations

  1. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  2. Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands

    Jan L.G. Dietz

  3. Henley Business School, University of Reading, Reading, United Kingdom

    Kecheng Liu

  4. INSTICC, Setubal, Portugal

    Joaquim Filipe

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Lindner, S. (2015). Two-Tier Machine Learning Using Conditional Random Fields with Constraints. In: Fred, A., Dietz, J., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2013. Communications in Computer and Information Science, vol 454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46549-3_6

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  • DOI: https://doi.org/10.1007/978-3-662-46549-3_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46548-6

  • Online ISBN: 978-3-662-46549-3

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