CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Domain Adaptation in NLP based on Hybrid Generative and Discriminative Model
Liu Kang; Zhao Jun
Source PublicationChinese Conference one Pattern Recognition
2008
Issue2008Pages:1-6
AbstractThis study investigates the domain adaptation problem for nature language processing tasks in the distributional view. A novel method is proposed for domain adaptation based on the hybrid model which combines the discriminative model with the generative model. The advantage of the discriminative model is to have lower asymptotic error, while the advantage of the generative model can easily incorporate the unlabeled data for better generalization performance. The hybrid model can integrate their advantages. For domain transfer, the proposed method exploits the difference of the distributions in different domains to adjust the weights of the instances in the training set so that the source labeled data is more adaptive to the target domain. Experimental results on several NLP tasks in different domains indicate that our method outperforms both the traditional supervised learning and the semi-supervised method.
KeywordDomain Adaptation
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20666
Collection模式识别国家重点实验室_自然语言处理
Recommended Citation
GB/T 7714
Liu Kang,Zhao Jun. Domain Adaptation in NLP based on Hybrid Generative and Discriminative Model[J]. Chinese Conference one Pattern Recognition,2008(2008):1-6.
APA Liu Kang,&Zhao Jun.(2008).Domain Adaptation in NLP based on Hybrid Generative and Discriminative Model.Chinese Conference one Pattern Recognition(2008),1-6.
MLA Liu Kang,et al."Domain Adaptation in NLP based on Hybrid Generative and Discriminative Model".Chinese Conference one Pattern Recognition .2008(2008):1-6.
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