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Automatic Keyphrases Extraction from Document Using Neural Network

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Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

Abstract

Keyphrase extraction is a task with many applications in information retrieval, text mining, and natural language processing. In this paper, a keyphrase extraction approach based on neural network is proposed. To determine whether a phrase is a keyphrase, the following features of a phrase in a given document are adopted: its term frequency and inverted document frequency, whether to appear in the title or headings (subheadings) of the given document, and its frequency appearing in the paragraphs of the given document. The algorithm is evaluated by the standard information retrieval metrics of precision and recall, and human assessment.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Jiabing Wang, Hong Peng & Jing-song Hu

Authors
  1. Jiabing Wang
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  2. Hong Peng
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  3. Jing-song Hu
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Editor information

Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, J., Peng, H., Hu, Js. (2006). Automatic Keyphrases Extraction from Document Using Neural Network. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_66

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  • DOI: https://doi.org/10.1007/11739685_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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