【作者】 康旭珍； 李双红； 李茹；

【Author】 KANG Xuzhen,LI Shuanghong,LI Ru School of Computer & Information Technology,Shanxi University,Taiyuan 030006,China

【机构】 山西大学计算机与信息技术学院；

【摘要】 本文基于汉语框架网,用框架核心依存图形式化的表示一个汉语句子,使得对句子能够进行深层语义理解。为了得到框架核心依存图,需要提取框架元素的语义核心词。文中使用条件随机场模型和最大熵模型来识别框架元素语义核心词,并分别对两个不同的模型所选的特征集进行了对比分析,且通过构造不同的特征模板进行对比实验,选取了较优的特征模板和模型。结果表明,CRF模型具有较好的识别性能,它在最优的特征模板下,对简单型和复合型短语类型框架元素语义核心词识别的平均正确率分别达到了96.45%和95.17%。更多还原

【Abstract】 In this paper,a Chinese sentence is formalized by the Frame Kernel Dependency Graph,which makes the deep semantic understanding of the sentence.To obtain the Frame Kernel Dependency Graph,it is necessary to extract the semantic core words of Frame Elements.This paper uses Conditional Random Fields model and Maximum entropy model to extract the semantic core words of Frame Elements,and feature sets with respect to these different two models were compared and different feature templates setting were conducted,selected the optimum template and model.Experimental results showed that CRF model has better performance to this research.With the optimal feature template,the average precision of experiment result achieved 96.45%and 95.17%for frame elements of simple and complex phrase type respectively.更多还原