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The Research on Chinese Coreference Resolution Based on Support Vector Machines

机译:基于支持向量机的中国Coreference解决研究

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摘要

Coreference is a common linguistic phenomenon in natural language understanding, it plays an important role in simplifying the expression and linking up the context. In this paper, the algorithm of support vector machines is applied to solve the problem of Chinese coreference, we consider fully the important characteristics which related to coreference and integrate them effectively to build model. In the handling of training data, using data scaling techniques balance the range of characteristic values, and use cross validation to optimize the training parameters of the model. The experimental results show that the F-score of positive instances and negative instances reached 76.80% and 90.91% respectively on the classification model in Lancaster Corpus of Mandarin Chinese.
机译:Coreference是一种常见的语言现象,在自然语言理解中,它在简化表达式并连接上下文方面发挥着重要作用。本文应用了支持向量机的算法来解决中国芯参考的问题,我们考虑完全与Coreference相关的重要特征,并有效地集成到构建模型。在处理培训数据的处理中,使用数据缩放技术平衡特征值的范围,并使用交叉验证来优化模型的训练参数。实验结果表明,普通实例的正面情况和负实例的F分数分别达到76.80%和90.91%,普通话中的普通话中的兰卡斯特语料库分别为76.80%和90.91%。

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