Neural Network Approach to Adaptive Learning with an Application to Chinese Homophone Disambiguation

Abstract

International Journal of Computer Processing of LanguagesVol. 15, No. 03, pp. 245-260 (2002) No AccessNeural Network Approach to Adaptive Learning with an Application to Chinese Homophone DisambiguationYUE-SHI LEEYUE-SHI LEEDepartment of Information Management, Ming-Chuan University, 5, The-Ming Rd., Gwei-Shan District, Tao-Yuan County 333, Taiwan, R.O.C. Search for more papers by this author https://doi.org/10.1142/S0219427902000662Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThe 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 are to be adjusted and how to adjust their probabilities by a neural network model. The resulting techniques are greatly simplified and robust. The experimental results demonstrate the effects of the learning algorithm from generic domain to specific domain. A methodology is also presented to show how these techniques can be extended to various language models and corpus-based applications.Keywords:Adaptive LearningChinese Homophone DisambiguationConnectionist LearningCorpus-Based Language ModelRun-Time Context Domain FiguresReferencesRelatedDetails Recommended Vol. 15, No. 03 Metrics History KeywordsAdaptive LearningChinese Homophone DisambiguationConnectionist LearningCorpus-Based Language ModelRun-Time Context DomainPDF download