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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文共指解析研究

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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:共指是自然语言理解中的一种常见的语言现象,它在简化表达和联系上下文方面起着重要作用。本文采用支持向量机算法来解决中文共指问题,充分考虑了与共指有关的重要特征,并将其有效地集成到模型中。在训练数据的处理中,使用数据缩放技术可以平衡特征值的范围,并使用交叉验证来优化模型的训练参数。实验结果表明,在普通话兰开斯特语料库的分类模型上,正例和负例的F得分分别达到76.80%和90.91%。

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