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Finding Correlative Associations among News Topics

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Computational Linguistics and Intelligent Text Processing (CICLing 2001)

Abstract

A method for finding real-world associations between news topics (as distinguished from apparent associations caused by the constant size of the newspaper) is described. This is important for studying society interests.

The work was done under partial support of CONACyT, REDII, and SNI, Mexico.

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References

  1. Allan, J., Papka, R., and Lavrenko, V. (1998), Proc. Fo the 21st. ACM-SIGIR International Conference on Research and Development in Information Retrieval, Australia, 1998.

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  2. García-Menier E., “Un Sistema para la Clasificación de Notas Periodisticas”, Memorias del Simposium Internacional de Computación CIC-98, México, D. F., 1998.

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© 2001 Springer-Verlag Berlin Heidelberg

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Montes-y-Gómez, M., López-López, A., Gelbukh, A. (2001). Finding Correlative Associations among News Topics. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2001. Lecture Notes in Computer Science, vol 2004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44686-9_53

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  • DOI: https://doi.org/10.1007/3-540-44686-9_53

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41687-6

  • Online ISBN: 978-3-540-44686-6

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