框架元素语义核心词自动识别研究

该文基于汉语框架网,利用框架核心依存图形式化地表示一个汉语句子,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,需要提取其中框架元素的语义核心词。该文较为系统地描述了框架元素的语义核心词的识别问题。我们利用条件随机场模型、最大熵模型和支持向量机模型来识别框架元素语义核心词,并分别对这三种不同的模型所选的特征集进行了分析,且通过构造不同的特征模板进行对比实验,选取其中较优的特征模板和模型。结果表明,条件随机场模型具有较好的识别性能,在对其特征模板做进一步改进的基础上,识别效率也得到一定的提高。其中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97.34%和94.03%。

Automatic Identification of Semantic Core Words for the Frame Element

The Frame Kernel Dependency Graph based on the Chinese FrameNet is adopted to convey the deep semantic understanding of a Chinese sentence. The Frame Kernel Dependency Graph is to be obtained by extracting the semantic core words of Frame Elements. The identification of the semantic core words of Frame Element is investigated by the Conditional Random Fields, the Maximum Entropy and the Support Vector Machine models. Various feature sets with respect to these three models are analyzed and different feature template settings are compared to select the optimum template and model. Experimental results show that the CRF model has the best performance. When its feature template is improved further, the results also increase to some extent. The average precision of experiment result achieves 97.34% and 94.03% for Frame Elements of simple and complex phrase type, respectively.
Key wordsframe elements;frame kernel dependency graph;conditional random fields;maximum entropy model;support vector machine model