According to Kripke, traditional approaches to presupposition and anaphora do not work in some compound sentences because traditionally, they have been thought to assign presuppositions to each clause in isolation. I ...According to Kripke, traditional approaches to presupposition and anaphora do not work in some compound sentences because traditionally, they have been thought to assign presuppositions to each clause in isolation. I agree with this criticism, but also think that context is required in order to determine the presupposition included in a complex sentence. To show the context role in fixing the utterance truth-conditions in those cases, this paper introduces the Kripkean anaphoric account on presuppositions （section 2）, and then criticizes this approach because it is very restrictive （section 3）. The paper concludes that to solve those difficulties, one should appeal to a two-dimensional framework, including a new parameter that focuses on the sentence according to the concrete features of the context： the speaker＇s point of view （section 4）.展开更多
It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in de...It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.展开更多
文摘According to Kripke, traditional approaches to presupposition and anaphora do not work in some compound sentences because traditionally, they have been thought to assign presuppositions to each clause in isolation. I agree with this criticism, but also think that context is required in order to determine the presupposition included in a complex sentence. To show the context role in fixing the utterance truth-conditions in those cases, this paper introduces the Kripkean anaphoric account on presuppositions （section 2）, and then criticizes this approach because it is very restrictive （section 3）. The paper concludes that to solve those difficulties, one should appeal to a two-dimensional framework, including a new parameter that focuses on the sentence according to the concrete features of the context： the speaker＇s point of view （section 4）.
基金Projects(41001260,61173122,61573380) supported by the National Natural Science Foundation of ChinaProject(11JJ5044) supported by the Hunan Provincial Natural Science Foundation of China
文摘It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.
