ID 原文 译文
40326 问答系统是一种以准确且自然的语言来回答用户提问的系统。 The Q & A system is a kind of system which can answer user's questions with accurate and natural language.
40327 本文对其中涉及的“命名实体识别”环节尝试了一些改进措施：针对传统单向模板匹配耗时耗力的问题，提出一种双向格子结构的长短时记忆网络(Lattice Bi-LSTM)，解决了命名实体识别中对句子处理不当和对分词结果具有依赖性的两大问题。 Some improvement measures have been tried for“named entity recognition”. Aiming at the time- and labor-consuming problem of traditional one-way template matching, this paper proposes a lattice bi-directional structure of long short-term memory(Lattice Bi-LSTM)network, which solves the problems of improper sentence processing and dependence on the result of word segmentation in named entity recognition.
40328 与单向结构相比，双向结构能更好地利用句子信息，使输出结果更具鲁棒性，从而更准确地捕获语义信息； Compared with the unidirectional structure, the bi-directional structure can make better use of sentence information and make the output more robust, thus capturing semantic information more accurately.
40329 针对传统方法未考虑实体间相似度的非线性耦合性问题，提出一种利用周期性核函数将“相似”实体准确链接到知识库中去的方法。 To solve the problem of non-linear coupling of similarity between entities in traditional methods, a method is proposed to link“similar”entities to the knowledge base accurately by using periodic kernel function.
40330 对提出的两种改进方法进行了实验验证，结果表明：与经典方法相比，所用方法具有显著的改进效果。 The two improved methods are verified by experiments, whose results show that they have significant improvement effects compared with the classical method.
40331 提出了一种新的物理主机资源利用阈值边界管理策略(Physical host resource utilization thresholds management strategy，RUT-MS)。 A new physical resource utilization thresholds management strategy called RUT-MS for cloud data centers is proposed in this paper.
40332 RUT-MS把云数据中心的虚拟机迁移过程进一步划分为超负载主机检测、虚拟机选择、虚拟机放置第1阶段、低负载主机检测和虚拟机放置第2阶段。 In RUT-MS, the virtual machine migration process is divided into five steps: Overloaded host status detection, virtual machine selection, the first virtual machine placement, under-loaded host status detection and the second virtual machine placement phases.
40333 使用一种迭代权重线性回归方法来预测物理资源的阈值上限，避免超负载的物理主机数量的增加； In overloading host detection, RUT-MS uses an iterative weighted linear regression method to determine two utilization thresholds so that the performance degradation is avoided.
40334 采用最小能量消耗策略完成虚拟机选择过程；使用多维物理资源的均方根来确定其资源使用阈值下限，减少低负载物理主机数量。 Maximum power reduction policy(MPR)is adopted in virtual machine selection phase. A vector magnitude squared of multiple dimension resources is used to decide the low resource utilization thresholds and switch them to a power saving mode.Finally, RUT-MS is evaluated using CloudSim with real-world workload data.
40335 实验结果表明：RUT-MS物理资源利用阈值边界管理策略使云数据中心的能量消耗和虚拟机迁移次数明显减少，SLA(Service level agreement)违规率和SLA及能量消耗联合指标只有少量的增加。 Simulation results show that RUT-MS can reduce the energy cost incurred on the system due to migration. The reduced number of virtual machine migrations and improved energy-efficiency are also obtained in our test. Moreover, the service level agreement(SLA)violation and SLA for energy united metrics are also good.