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Abstract

Increasingly, companies are using the Internet to present themselves to customers and provide most up-to-date information and offerings. Hence it can be used to gain competitive information on a regular and timely basis. However, right now this process is mainly based on manual searches and is thus very costly and time consuming. This chapter discusses technical approaches supporting this process.

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References

  1. Baeza-Yates, R.; Ribeiro-Neto, B.: Modern Information Retrieval, Addison Wesley Publishing Co., 1999

    Google Scholar 

  2. Brigitte Endres-Niggemeyer (ed): Summarizing Information, Springer, Berlin, 1998

    Google Scholar 

  3. Goldstein J.; Kantrowitz, M.; Carbonell, J.: Summarizing Text Documents: Sentence Selection and Evaluation Metrics. In: Proceedings SIGIR ‘89, ACM, Berkeley, CA, 1999

    Google Scholar 

  4. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: European Conference on Machine Learning (ECML), 1998

    Google Scholar 

  5. Koller, D.; Sahami, M.: Hierarchically classifying documents using very few words. In: International Conference on Machine Learning (ICML), 1997

    Google Scholar 

  6. Mani, I.; Maybury M.T. (eds.): Advances in automatic Text Summarization, MIT Press, Cambridge, MA, 1999

    Google Scholar 

  7. Sparck Jones, K.; Willet, P. (eds): Readings in Information Retrieval, Morgan Kaufmann Publishers, 1997

    Google Scholar 

  8. Warren, January 14: I-Spy - Getting the low-down on your competition is just a few clicks away, Special Report: E-Commerce, google.com/, 2002

    Google Scholar 

  9. Witten, I.H.; Moffat, A.; Bell T. C.: Managing Gigabytes: compressing and indexing documents and images, Morgan Kaufmann Publishers Inc, 1999

    Google Scholar 

  10. Yang, Y.: An Evaluation of Statistical Approaches to Text Categorization Information Retrieval, May 1999

    Google Scholar 

  11. Yang Y.; Pedersen J.: A comparative study on feature selection in text categorization. In: International Conference on Machine Learning (ICML), 1997

    Google Scholar 

  12. http://www.acm.org/sigir (Int. Conference on Research and Development in IR)

  13. http://www.acm.org/sigkdd (Int. Conf. on Knowledge Discovery and Data Mining)

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© 2003 Springer Science+Business Media New York

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Gerstl, P., Kuhn, B., Novak, HJ. (2003). A Web-Based Approach to Competitive Intelligence. In: Abramowicz, W. (eds) Knowledge-Based Information Retrieval and Filtering from the Web. The Springer International Series in Engineering and Computer Science, vol 746. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3739-4_14

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  • DOI: https://doi.org/10.1007/978-1-4757-3739-4_14

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5376-6

  • Online ISBN: 978-1-4757-3739-4

  • eBook Packages: Springer Book Archive

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