Taking the personal experience of Robert Morrison into account, this paper focuses on the rapport between the author and his works. As the first Protestant missionary to China, Robert Morrison endeavored to learn the ...Taking the personal experience of Robert Morrison into account, this paper focuses on the rapport between the author and his works. As the first Protestant missionary to China, Robert Morrison endeavored to learn the Chinese language and to collect local cultural information, so as to encyclopedically present his knowledge of Chinese through the language reference books he compiled. This paper examines the range of linguistic registers of the represented examples from Morrison＇s dictionaries, so as to discuss the way in which various registers are combined into the text and how they are related with different social arenas. Placing Morrison＇s works in a wider social and intellectual context, this paper also discusses issues of cultural exchange between China and early nineteenth-century Europe.展开更多
In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great po...In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.展开更多
文摘Taking the personal experience of Robert Morrison into account, this paper focuses on the rapport between the author and his works. As the first Protestant missionary to China, Robert Morrison endeavored to learn the Chinese language and to collect local cultural information, so as to encyclopedically present his knowledge of Chinese through the language reference books he compiled. This paper examines the range of linguistic registers of the represented examples from Morrison＇s dictionaries, so as to discuss the way in which various registers are combined into the text and how they are related with different social arenas. Placing Morrison＇s works in a wider social and intellectual context, this paper also discusses issues of cultural exchange between China and early nineteenth-century Europe.
基金Project (No. 5959438) supported by Microsoft (China) Co., Ltd
文摘In standard canonical correlation analysis (CCA), the data from definite datasets are used to estimate their canonical correlation. In real applications, for example in bilingual text retrieval, it may have a great portion of data that we do not know which set it belongs to. This part of data is called unlabeled data, while the rest from definite datasets is called labeled data. We propose a novel method called regularized canonical correlation analysis (RCCA), which makes use of both labeled and unlabeled samples. Specifically, we learn to approximate canonical correlation as if all data were labeled. Then, we describe a generalization of RCCA for the multi-set situation. Experiments on four real world datasets, Yeast, Cloud, Iris, and Haberman, demonstrate that, by incorporating the unlabeled data points, the accuracy of correlation coefficients can be improved by over 30%.
