We focused in this study on two verbs of motion ba （come） and hevi （bring） used in contemporary Hebrew pointing to a number of semantic shifts occurring in each of them and to categorical shifts that occurred in t...We focused in this study on two verbs of motion ba （come） and hevi （bring） used in contemporary Hebrew pointing to a number of semantic shifts occurring in each of them and to categorical shifts that occurred in the verb ha. We conducted a semantic and syntactic analysis of these shifts in which we observed： a change in the syntactic valuation of ba and hevi, the semantic characteristic of the nominal collocations which form their syntactic setting, and the semantic connection between their original and new meanings. The article starts out with a presentation of the original meanings of the two verbs as belonging to the family of concrete verbs of motion. It then presents the semantic shifts each undergoing from designating motion to designating giving, existing, and modality （capability, intent and aspect） and concludes with the categorical shift of the verb ba to impersonal （ロ＂λ∏） and to discourse marker. It is noteworthy that in each of the shifts observed we noticed relation between the meaning stemming from the shift and the original meaning of ba and hevi as verbs of motion. We were able to prove that the original meaning is still echoed both in the semantic and category categorical shifts.展开更多
We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the l...We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis （pLSA） based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ＇final＇ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.展开更多
文摘We focused in this study on two verbs of motion ba （come） and hevi （bring） used in contemporary Hebrew pointing to a number of semantic shifts occurring in each of them and to categorical shifts that occurred in the verb ha. We conducted a semantic and syntactic analysis of these shifts in which we observed： a change in the syntactic valuation of ba and hevi, the semantic characteristic of the nominal collocations which form their syntactic setting, and the semantic connection between their original and new meanings. The article starts out with a presentation of the original meanings of the two verbs as belonging to the family of concrete verbs of motion. It then presents the semantic shifts each undergoing from designating motion to designating giving, existing, and modality （capability, intent and aspect） and concludes with the categorical shift of the verb ba to impersonal （ロ＂λ∏） and to discourse marker. It is noteworthy that in each of the shifts observed we noticed relation between the meaning stemming from the shift and the original meaning of ba and hevi as verbs of motion. We were able to prove that the original meaning is still echoed both in the semantic and category categorical shifts.
基金Project supported by the Fundamental Research Funds for the Central Universities,China(No.lzujbky-2013-41)the National Natural Science Foundation of China(No.61201446)the Basic Scientific Research Business Expenses of the Central University and Open Project of Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education,Lanzhou University(No.LZUMMM2015010)
文摘We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis （pLSA） based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ＇final＇ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.
