Skip to main content
SpringerLink
Log in

Where to Position the Precision in Knowledge Extraction from Text

  • Conference paper
  • First Online:
Book cover Engineering of Intelligent Systems (IEA/AIE 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2070))

  • 706 Accesses

Abstract

This paper concerns knowledge extraction for applications concerning the automated filling of templates from an input of semi-structured textual documents. The template filling task can be viewed as a collaboration between a number of agents, including NE-Agents that are specialised to detect occurrences of specific features in the text and TE-Agents that specialise at combining the results from multiple NE-Agents in order to create a template instance. This paper presents an automated learning approach for the generation of a TE-Agent that extracts spatial relationships between the various features of a template. It is shown that this TE-Agent can compensate for imprecise performance on the part of the NE-Agents.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Relational Learning of Pattern-Match Rules for Information Extraction, M E Califf and R J Mooney, Proceedings ACL-97: Workshop on Natural Language Learning

    Google Scholar 

  2. LearningPinocchio, Ciravegnd F, http://ecate.itc.it:1025/cirave/LEARNING/JOBS/home.html

  3. Information Extraction a User Guide, H Cunningham, CS-99-07, Dept. Of Comp Sci., Uni. Of Sheffield, 1999

    Google Scholar 

  4. STALKER: Learning Extraction Rules for Semistructured, Web-based Information Sources-Muslea I, Minton S, Knoblock C, AAAI’98 Workshop “AI and Information Integration”

    Google Scholar 

  5. Text Mining-Knowledge extraction from unstructured textual data, M Rajman and Besançon-6th Conf. of International Federation of Classification Societies

    Google Scholar 

  6. Information Extraction as a Basis for High-Precision Text Classification, E Riloff and W Lehnert, ACM Transactions on Information Systems vol. 12 no. 3 1994.

    Google Scholar 

  7. Learning Information Extraction Rules for Semi-Structured and Free Text, S Sonderland, Machine Learning 34, 233–272, 1999

    Google Scholar 

  8. Information Extraction from HTML: Combining XML and standard Techniques for IE from the Web, L Xiao, 2000

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Interprice Technologies GmbH, Berlin, Germany

    Luo Xiao & Dieter Wissmann

  2. Dept. of Computer Sciences VI (IMMD VI), University of Erlangen-Nuremberg, Germany

    Michael Brown

  3. Siemens AG, CT SE 5, Erlangen, Germany

    Stefan Jablonski

Authors
  1. Luo Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dieter Wissmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Brown
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stefan Jablonski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Hungarian Academy of Sciences, Intelligent Manufacturing and Business Processes Computer and Automation Research Institute, Kende utca 13-17, 1111, Budapest, Hungary

    László Monostori  & József Váncza  & 

  2. Department of Computer Science 601 University Drive, Southwest Texas State University, San Marcos, TX, 78666-4616, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xiao, L., Wissmann, D., Brown, M., Jablonski, S. (2001). Where to Position the Precision in Knowledge Extraction from Text. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_22

Download citation

  • DOI: https://doi.org/10.1007/3-540-45517-5_22

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42219-8

  • Online ISBN: 978-3-540-45517-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation