Discourse markers have been studied within the relevance theoretical framework in this paper. According to relevance theory, every act of ostensive communication communicates a presumption of its own optimal relevance...Discourse markers have been studied within the relevance theoretical framework in this paper. According to relevance theory, every act of ostensive communication communicates a presumption of its own optimal relevance. The communicator, who is aiming at optimal relevance, must direct the audience to a particular set of assumptions by using discourse markers. Whereas, from the audience's point of view, discourse markers can minimize his contextual assumptions in looking for relevance, thus the pragmatic inferential process will be much easier.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Discourse markers have been studied within the relevance theoretical framework in this paper. According to relevance theory, every act of ostensive communication communicates a presumption of its own optimal relevance. The communicator, who is aiming at optimal relevance, must direct the audience to a particular set of assumptions by using discourse markers. Whereas, from the audience's point of view, discourse markers can minimize his contextual assumptions in looking for relevance, thus the pragmatic inferential process will be much easier.
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%.