Named entity relation extraction based on SVM training by positive and negative cases
基于正反例训练的SVM命名实体关系抽取

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.