An Adaptive Algor ithm for Learning Changes in Run-Time Context Domain
نویسندگان
چکیده
The run-time context domain has much effect on the performance of practical corpus-based applications. Previous smoothing techniques, and class-based and similarity-based models cannot handle the dynamic status perfectly. In this paper, an adaptive learning algorithm is proposed for task adaptation to fit best the run-time context domain in the application of Chinese homophone disambiguation. It shows which objects to be adjusted and how to adjust their probabilities by a neural network model. The experimental results demonstrate the effects of the learning algorithm from generic domain to specific domain.
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