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Improved Automatic Keyword Extraction Based on TextRank Using Domain Knowledge

  • Conference paper
Book cover Natural Language Processing and Chinese Computing (NLPCC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 496))

Abstract

Keyword extraction of scientific articles is beneficial for retrieving scientific articles of a certain topic and grasping the trend of academic development. For the task of keyword extraction for Chinese scientific articles, we adopt the framework of selecting keyword candidates by Document Frequency Accessor Variety(DF-AV) and running TextRank algorithm on a phrase network. To improve domain adaption of keyword extraction, we introduce known keywords of a certain domain as domain knowledge into this framework. Experimental results show that domain knowledge can improve performance of keyword extraction generally.

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Author information

Authors and Affiliations

  1. Key Laboratory of Computational Linguistics, Ministry of Education, Institute of Computational Linguistics, School of Electronics Engineering and Computer Science, Peking University, China

    Guangyi Li & Houfeng Wang

Authors
  1. Guangyi Li
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  2. Houfeng Wang
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Editor information

Editors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Chengqing Zong

  2. Dept. of Computer Science and Operations Research, University of Montreal, Montreal, Quebec, Canada

    Jian-Yun Nie

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Institute of Computer Science & Technology, Peking University, 100871, Beijing, China

    Yansong Feng

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Li, G., Wang, H. (2014). Improved Automatic Keyword Extraction Based on TextRank Using Domain Knowledge. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_36

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  • DOI: https://doi.org/10.1007/978-3-662-45924-9_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45923-2

  • Online ISBN: 978-3-662-45924-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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