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- 2015
一种基于树核函数的半监督关系抽取方法研究
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Abstract:
摘要： 为了解决传统的半监督关系抽取算法易产生的"语义变异"问题,提出一种新的基于树核函数的半监督关系抽取算法。该算法主要采用树核函数和种子集约束扩展两个策略,弱化"语义变异"现象带来的关系抽取不够准确的问题,提高关系识别的正确率。在基准数据集PopBank上的试验研究表明,提出的使用约束机制扩充种子集的半监督学习方法在4个评价指标上(Precision, Recall, F-measure, Accuracy)均优于常用的两种关系抽取方法,从而验证了该算法与其他算法相比能够具有较好的关系抽取能力。
Abstract: It was difficult for traditional semi-supervised relation extraction methods to solve "semantic variation" problem. A new semi-supervised relation extraction algorithm based on ensemble learning was prorosed and named L-EC-RE, which used two strategies, one was tree kernel and the other was constrained extension seed set. Experimental study on PopBank benchmark data sets showed that L-EC-RE had better performance than two usual relation extraction algorithms in four assessment criteria, which were Precision, Recall, F-measure and Accuracy
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