基于多分类SVM-KNN的实体关系抽取方法

实体关系抽取是信息抽取领域的重要研究课题之一。传统的实体关系抽取研究注重于从实体对出现的上下文中提取词法和语义等特征,然后利用分类器(如SVM)进行实体关系抽取,但该类方法忽略了分类器对实体抽取性能的影响。针对SVM分类器对超平面附近样本分类正确率低的问题,本文设计了一种基于双投票机制的SVM模糊样本选择方法。在此基础上,对确定区域样本直接使用SVM分类器进行分类,并利用KNN算法对模糊区域样本进行二次分类。在SemEval-2010评测任务提供的实体关系抽取数据上进行实验,实验结果表明该方法能较大提高实体关系抽取的性能。

Entity Relation Extraction Method Based on Multi-SVM-KNN Classifier

Entity relation extraction is one of the most important researches in the field of information extraction. Previous researches focus on extracting various kinds of lexical or semantic features from the context where the related entities appeared, and one kind of classifiers (such as SVM) is used to extract the entity relation, but this kind of methods ignore the impact of the classifier performance on the entity relation extraction. Since SVM classifier has low accuracy for the test samples near the hyperplane, a method based on double-vote mechanism is designed for determining the fuzzy SVM samples. In the method, SVM classifier is used to classify the non-fuzzy samples directly; then, k-nearest neighbors (KNN) algorithm is applied to classify the fuzzy ones. The experiment on the data provided by SemEval-2010 evaluation task shows that the method can improve the performance of the entity relation extraction.