基于正反例训练的SVM命名实体关系抽取

根据中文命名实体关系抽取的特点，从中文的形态学、语法及语义等几个方面选取特征并构建特征向量，然后将符合特定实体关系模板的候选命名实体对抽取出来并分为正反例。利用正反例样本对支持向量机(SVM)抽取器进行训练，以此来判断候选命名实体对的关系类型。实验证明，本方法能够有效提高中文命名实体关系抽取的准确率。

Named entity relation extraction based on SVM training by positive and negative cases

Based on the characteristics of the Chinese named entity relation extraction, features were selected and feature vectors were constructed in terms of Chinese morphological, grammar and semantics. Then potential named entity pairs in accordance with the specific entity relation template were extracted and divided into positive and negative cases. Support Vector Machine (SVM) classifier was trained by the positive and negative cases and used to judge the relation of the potential named entity pairs. Experimental results prove that this new method can effectively improve the accuracy of Chinese named entity relation extraction.