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This article addresses the issue of acquiring translation rules for machine translation (MT) systems that adopt a transfer approach. These rules aer semantic pattern pairs (SPPs) of the source and target languages. Practical MT systems must additionally contain a huge number of SPPs corresponding to rarely-used predicates and predicate usages. Such SPPs are difficult to automatically acquire with corpus-based methods. To solve this difficulty, this article proposes a method to acquire SPPs by using queries based on a semantic hierarchy. The proposed method asks a lexicographer for the necessary information in order to generalize the conditions of SPPs and then gradually generalizes these conditions. Experimental results show that the proposed method allows the acquisition of more plausible conditions within almost the same time spent for manual generalization.

Smart Educational Learning (SEL) has recently opened its ways in various changes in scientific discoveries, informatics, globalization, astronautics production, robotics, and artificial intelligence in the Higher Education System. In such an educational system, managing resources to increase education quality with an effective interactive environment has been considered a significant challenging factor for the Students and Teachers. Hence, in this paper, the internet of things assisted Interactive system (IoT-IS) for Smart Learning is used to measure the teachers’ and students’ performance analysis in the SEL platform. The psychometric processes with standards for effective teaching using smart educational learning tools have been discussed based on the higher education system requirements. Furthermore, an active learning strategy with an attention scoring method promotes students’ performance assessment in the higher education system using the interactive system. Facial expression detection and analysis are used and applied in online classroom videos in the SEL. Based on this detection and analysis, the attention of the students is observed. The experimental results show that the method enhances the student performance ratio of 98.5%, an accuracy ratio of 95.3%, an efficiency ratio of 96.7%, a reliability ratio of 93.2%, and a probability ratio of 94.5% compared to other existing methods.