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Extraction of Text Phrases Using Hierarchical Grammar

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Advances in Artificial Intelligence (Canadian AI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2338))

Abstract

This paper presents an algorithm for extraction of phrases from text documents. The algorithm builds phrases by iteratively merging bigrams according to an association measure. Two association measures are presented: mutual information and t-test. The extracted phrases are tested in a document classification task using a tf/idf model and a k-nearest neighbor classifier.

This work was partially supported by NSERC strategic grant and TL-NCE.

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References

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Author information

Authors and Affiliations

  1. Pattern Analysis and Machine Intelligence Lab, Department of Systems Design Engineering, University of Waterloo, Waterloo, Ont., Canada

    Jan Bakus & Mohamed Kamel

  2. Department of Management Sciences, University of Waterloo, Waterloo, Ont., Canada

    Tom Carey

Authors
  1. Jan Bakus
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  2. Mohamed Kamel
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  3. Tom Carey
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Editor information

Editors and Affiliations

  1. Computer Science, University of Waterloo, 200 University Ave.W., Waterloo, Ontario, Canada, N2L 3G1

    Robin Cohen

  2. IIT - e-Business, National Research Council, Incutech Brunswick, 2 Garland Court, Frederiction, New Brunswick, Canada, E3B 6C2

    Bruce Spencer

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© 2002 Springer-Verlag Berlin Heidelberg

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Bakus, J., Kamel, M., Carey, T. (2002). Extraction of Text Phrases Using Hierarchical Grammar. In: Cohen, R., Spencer, B. (eds) Advances in Artificial Intelligence. Canadian AI 2002. Lecture Notes in Computer Science(), vol 2338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47922-8_27

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  • DOI: https://doi.org/10.1007/3-540-47922-8_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43724-6

  • Online ISBN: 978-3-540-47922-2

  • eBook Packages: Springer Book Archive

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