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From Computational Intelligence to Web Intelligence
November 2002 (vol. 35 no. 11)
pp. 72-76
Nick Cercone, Dalhousie University
Vlado Keselj, Dalhousie University
Aijun An, York University
Kanlaya Naruedomkul, Mahidol University
Xiaohua Hu, DMW Software
Systems that can communicate naturally and learn from interactions will power Web intelligence?s longterm success. The large number of problems requiring Webspecific solutions demand a sustained and complementary effort to advance fundamental machinelearning research and incorporate a learning component into every Internet interaction.Traditional forms of machine translation either translate poorly, require resources that grow exponentially with the number of languages translated, or simplify language excessively. Recent success in statistical, nonlinguistic, and hybrid machine translation suggests that systems based on these technologies can achieve better results with a large annotated language corpus.Adapting existing computational intelligence solutions, when appropriate for Web intelligence applications, must incorporate a robust notion of learning that will scale to the Web, adapt to individual user requirements, and personalize interfaces.
Citation:
Nick Cercone, Lijun Hou, Vlado Keselj, Aijun An, Kanlaya Naruedomkul, Xiaohua Hu, "From Computational Intelligence to Web Intelligence," Computer, vol. 35, no. 11, pp. 72-76, Nov. 2002, doi:10.1109/MC.2002.1046978
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