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To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer. However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language. In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language. The proposed method works for both single-source and multi-source crosslingual NER. For the latter, we further propose a similarity measuring method to better weight the supervision from different teacher models. Extensive experiments for 3 target languages on benchmark datasets well demonstrate that our method outperforms existing state-of-theart methods for both single-source and multisource cross-lingual NER.

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To improve the efficiency and productivity of modern manufacturing, the requirements for enterprises are discussed. The new emerged technologies such as cloud computing and internet of things are analyzed and the bottlenecks faced by enterprises in manufacturing big data analytics are investigated. Scientific workflow technology as a method to solve the problems is introduced and an architecture of scientific workflow management system based on cloud manufacturing service platform is proposed. The functions of each layer in the architecture are described in detail and implemented with an existing workflow system as a case study. The workflow scheduling algorithm is the key issue of management system, and the related work is reviewed. This paper takes the general problems of existing algorithms as the motivation to propose a novel scheduling algorithm called MP (max percentages) algorithm. The simulation results indicate that the proposed algorithm has performed better than the other five classic algorithms with respect to both the total completion time and load balancing level.