自组织中文语义映射网络的优化特征编码方法

本文介绍自组织中文语义映射网络,并分别基于集合论、代数理论和概率论研究和提出六种不同的特征编码方法,这对自组织语义映射效果有很重要的影响。通过性能评价得出如下结论:使用TFIDF修正的频率密度编码能得到最佳效果,其语义映射的精确度和召回率分别为94.4%和90.7% ,而基于向量模型的方法则都不适用于中文自组织语义映射。文中给出结果分析。另外比较实验结果表明文中的最好方法其系统性能好于目前广泛采用的分层聚类技术,并远好于多元统计分析技术,例如主成分分析的特征降维编码。

Optimizing Feature Encoding for Self- Organizing Chinese Semantic Maps

In this paper, we introduce self-organizing Chinese semantic map, then study and propose six different approaches of feature encoding which is crucial to the performance of a SOM. The approaches are based on set theory, algebra, and probabilistic theory respectively. We conclude from the evaluation results that the method of combining frequency density approach and TFIDF approach has the best performance with 94.4% of precision and 90.7% of recall on semantic mapping, and vector space oriented approaches are not suitable for the task. Analyses of results are also given. Comparative experiments show that the best approach in this paper is better than conventional hierarchy clustering technique, and much better than multivariate statistical analyses such as principle component analyses on dimension reduction based feature encoding.