Keyword Extraction Using Support Vector Machine

  • Kuo Zhang
  • Hui Xu
  • Jie Tang
  • Juanzi Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)


This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning’ of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information’, which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information’, but also ‘local context information’ for extracting keywords from documents. As far as we know, utilizing both ‘global context information’ and ‘local context information’ in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.


Support Vector Machine Context Feature Global Context Baseline Method Document Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Azcarraga2002]
    Azcarraga, A., Yap, T.J., Chua, T.S.: Comparing Keyword Extraction Techniques for WEBSOM Text Archives. International Journal of Artificial Intelligence Tools 11(2), 219–232 (2000)Google Scholar
  2. [Berger2000]
    Berger, A.L., Mittal, V.O.: OCELOT: A System for Summarizing Web Pages. In: Proceedings of the 23rd ACM SIGIR Conference, pp. 144–151 (2000)Google Scholar
  3. [Brill1999]
    Brill, E., Ngai, G.: Man vs. machine: A case study in baseNP learning. In: Proceedings of the 18th International Conference on Computational Linguistics, pp. 65–72 (1999)Google Scholar
  4. [Cunningham2002]
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In: Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics, Philadelphia (2002)Google Scholar
  5. [DUC]
    Document Understanding Conference,
  6. [Dumais1998]
    Dumais, S.T., Platt, J., Heckerman, D., Sahami, M.: Inductive Learning Algorithms and Representations for Text Categorization. In: Proceedings of the 7th International Conference on Information and Knowledge Management, pp. 148–155 (1998)Google Scholar
  7. [Frank1999]
    Frank, E., Paynter, G.W., Witten, I.H.: Domain-Specific Keyphrase Extraction. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 668–673. Morgan Kaufmann, San Francisco (1999)Google Scholar
  8. [Hulth2004]
    Hulth, A.: Combining Machine Learning and Natural Language Processing for Automatic Keyword Extraction. Ph.D. diss., Dept. of Computer and Systems Sciences, Stockholm University (2004) Google Scholar
  9. [Mani1999]
    Mani, I., Maybury, M.T.: Advances in Automatic Text Summarization. The MIT Press, Cambridge (1999)Google Scholar
  10. [Mani2001]
    Mani, I.: Automatic Summarization. John Benjamins Pub.Co., Amsterdam (2001)zbMATHGoogle Scholar
  11. [Matsuo04]
    Matsuo, Y., Ishizuka, M.: Keyword Extraction from a Single Document using Word Co-occurrence Statistical Information. Int’l Journal on Artificial Intelligence Tools 13(1), 157–169 (2004)CrossRefGoogle Scholar
  12. [Miller1990]
    Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Wordnet: An On-line Lexical Database. International Journal of Lexicography 3(4), 235–312 (1990)CrossRefGoogle Scholar
  13. [Sleator1991]
    Sleator, D., Temperley, D.: Parsing English with a Link Grammar. Technical Report, CMU-CS-91-196, Dept. of Computer Science, Carnegie Mellon University (1991) Google Scholar
  14. [Tang2004]
    Tang, J., Li, J.Z., Wang, K.H., Cai, Y.R.: Loss Minimization based Keyword Distillation. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 572–577. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. [Turney2000]
    Turney, P.D.: Learning Algorithms for Keyphrase Extraction. Information Retrieval 2(4), 303–336 (2000)CrossRefGoogle Scholar
  16. [Vapnik1995]
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)zbMATHGoogle Scholar
  17. [Witten1999]
    Witten, I.H., Paynter, G.W., et al.: KEA: Practical Automatic Keyphrase Extraction. In: Proceedings of 4th ACM Conference on Digital Libraries, Berkeley, CA, pp. 254–255 (1999)Google Scholar
  18. [Xun2000]
    Xun, E., Huang, C., Zhou, M.: A Unified Statistical Model for the Identification of English baseNP. In: Proceedings of the 38th Annual Meeting of the Association for ComputationalLinguistics, Hong Kong (2000)Google Scholar
  19. [Zha2002]
    Zha, H.: Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering. In: Proceedings of the 25th ACM SIGIR Conference, pp. 113–120 (2002)Google Scholar
  20. [Zhu2003]
    Zhu, M., Cai, Z., Cai, Q.: Automatic Keywords Extraction of Chinese Document Using Small World Structure. In: Proceeding of the international conference on Natural Language Processing and Knowledge Engineering (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kuo Zhang
    • 1
  • Hui Xu
    • 1
  • Jie Tang
    • 1
  • Juanzi Li
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingP.R.China

Personalised recommendations