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This paper studied the probabilistic hesitant fuzzy staged dynamic group decision-making problem with partially unknown weights of decision makers. Aiming at the change of the external environment and combining the symbol distance measurement, the paper proposed a phased group decision-making method considering the change of the external environment. Firstly, the method defined the hesitant degree formula of probabilistic hesitant fuzzy elements based on the variance and number difference of the elements, and defined the symbol distance formula of probabilistic hesitant fuzzy elements on this basis. Then, according to the characteristics that the change of the external environment would lead to the difference of the information obtained in each timing stage, the method constructed a dynamic decision model to determine the weights of decision makers in each timing stage, and then aggregated the information in stages to form a scheme chain of decision-making process. Finally, an example verified th e effectiveness and rationality of the proposed method. [ABSTRACT FROM AUTHOR]

Recognizing time phrases from clinical text is a fundamental task for many applications in clinical NLP. Traditional methods based on rules and machine learning require the design of complex rules and feature extraction, and the serial method used by most systems may lead to error propagation. This paper proposed a novel neural network based on bidirectional longshort term memory (BLSTM) to identifying clinical time expressions and the type of them simultaneously. Firstly, it combined character-level word embedding trained by convolutional neural network (CNN) with word embedding trained from large-scale biomedical corpus together as input to BLSTM. Then it utilized BLSTM to model context information of each word. Finally, it employed conditional random field (CRF) to optimize the output of BLSTM. This paper evaluated the model task 12 of on the Semeval-2016. It receives the best F1 value without requiring any handcrafted features or rules . Compared with the state-of-theart systems in this task, the proposed model improves the F1 scores by 3% . [ABSTRACT FROM AUTHOR]