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基于任务转化的事件抽取通用框架
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作者 李健 胡瑞娟 +1 位作者 张克亮 刘海砚 《计算机工程与应用》 CSCD 北大核心 2024年第15期133-142,共10页
论元分散、多重事件和论元重叠是事件抽取长期面临的挑战,许多任务还存在触发词或位置标注缺失的情况。对此,提出一种基于任务转化的事件抽取通用框架,包括任务转化、关系抽取和事件预测3个模块。有触发词和无触发词的标注事件分别被转... 论元分散、多重事件和论元重叠是事件抽取长期面临的挑战,许多任务还存在触发词或位置标注缺失的情况。对此,提出一种基于任务转化的事件抽取通用框架,包括任务转化、关系抽取和事件预测3个模块。有触发词和无触发词的标注事件分别被转化为不同形式的关系三元组;这些三元组将与原始文本一起作为关系抽取模型的训练数据;事件预测时先从输入文本中抽取三元组,再将它们还原为目标事件。对于训练语料中位置标注缺失的情况,设计了基于最短距离的论元定位算法。该框架在ChFinAnn数据集上取得81.6%的平均F1值,在DuEE-Fin数据集上的F1值为72.04%(在线提交结果),均达到目前的SOTA水平。实验结果表明,该框架不仅可以显著提高事件抽取效果,而且具有广泛的适应能力。 展开更多
关键词 事件抽取 通用框架 任务转化 论元定位
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Development of a Methodology for Determination and Analysis of Thermal Displacements of Machine Tools Using Finite Elements Method and Artificial Neural Network
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作者 Romualdo Figueiredo de Sousa Fracisco Augusto Vieira da Silva Joao Bosco Aquino Silva Jose Carlos de Lima Junior 《Journal of Mechanics Engineering and Automation》 2014年第6期488-498,共11页
In the processes of manufacturing, MT (machine tools) plays an important role in the manufacture of work pieces with complex and high dimensional and geometric accuracy. Much of the errors of a machine tool are thos... In the processes of manufacturing, MT (machine tools) plays an important role in the manufacture of work pieces with complex and high dimensional and geometric accuracy. Much of the errors of a machine tool are those which are thermally induced which are from internal and external heat sources acting on the machine. In this paper, a methodology for determining and analyzing the thermal deformation of machine tools using FEM (finite element method) and ANN (artificial neural networks) is presented. After modeling the machine using FEM is defined the location of the heat sources, it is possible to obtain the temperature gradient and the corresponding thermal deformation at predetermined periods. Results obtained with simulations using the software NX.7.5 showed that this methodology is an effective tool in determining the thermal deformation of the machine, correlating the temperature reading at strategic points with volumetric deformation at the tool tip. Therefore, the thermal analysis of the errors in the pair tool part can be established. After training and validation process, the network will be able to make the prediction of thermal errors just stating the temperature values of specific points of each heat source, providing a way for compensation of thermally induced errors. 展开更多
关键词 Thermal displacement machine tool finite element method artificial neural network.
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