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A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data

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Advances in Knowledge Discovery and Data Mining (PAKDD 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2637))

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Abstract

In this article we present a semi-supervised algorithm for pattern discovery in information extraction from textual data. The patterns that are discovered take the form of regular expressions that generate regular languages. We term our approach ‘semi-supervised’ because it requires significantly less effort to develop a training set than other approaches. From the training data our algorithm automatically generates regular expressions that can be used on previously unseen data for information extraction. Our experiments show that the algorithm has good testing performance on many features that are important in the fight against terrorism.

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References

  1. S. Soderland: Learning information extraction rules for semi-structured and free text. Machine Learning, 34(1–3):233–272. (1999)

    Article  MATH  Google Scholar 

  2. Eric Brill: Pattern-Based Disambiguation for Natural Language Processing. Proceedings of Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. (EMNLP/VLC-2000)

    Google Scholar 

  3. Christopher D. Manning and Hinrich Schütze: Foundations of Statistical Natural Language Processing, MIT Press. (2000)

    Google Scholar 

  4. Van Rijsbergen: Information Retrieval. Butterworths, London. (1979)

    Google Scholar 

  5. Michael Chau, Jennifer J. Xu, Hsinchun Chen: Extracting Meaningful Entities from Police Narrative Reports. Proceedings of the National Conference for Digital Government Research. Los Angeles, California. (2002)

    Google Scholar 

  6. Tianhao Wu, and William M. Pottenger: An extended version of “A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data”. Lehigh University Computer Science and Engineering Technical Report LU-CSE-03-001. (2003)

    Google Scholar 

  7. Hopcroft, J. and J. Ullman: Introduction to Automata Theory, Languages and Computation. Addison-Wesley. (1979)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, Lehigh University, USA

    Tianhao Wu & William M. Pottenger

Authors
  1. Tianhao Wu
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  2. William M. Pottenger
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Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea

    Kyu-Young Whang

  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea

    Jongwoo Jeon

  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea

    Kyuseok Shim

  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA

    Jaideep Srivastava

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© 2003 Springer-Verlag Berlin Heidelberg

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Wu, T., Pottenger, W.M. (2003). A Semi-supervised Algorithm for Pattern Discovery in Information Extraction from Textual Data. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_12

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  • DOI: https://doi.org/10.1007/3-540-36175-8_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-04760-5

  • Online ISBN: 978-3-540-36175-6

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