Nick CerconeLijun HouVlado KeseljAijun AnKanlaya NaruedomkulXiaohua HuDalhousie UniversityUniversity of WaterlooYork UniversityMahidol UniversityDMW Software2018-07-242018-07-242002-11-01Computer. Vol.35, No.11 (2002)001891622-s2.0-0036849394https://repository.li.mahidol.ac.th/handle/20.500.14594/20141Systems that can communicate naturally and learn from interactions will power Web intelligence's long-term success. The large number of problems requiring Web-specific solutions demand a sustained and complementary effort to advance fundamental machine-learning research and incorporate a learning component into every Inrernet 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.Mahidol UniversityComputer ScienceReviewSCOPUS10.1109/MC.2002.1046978