自然语义特征的模糊聚类分析  

Analysis of natural semantic feature with fuzzy clustering

作　　者：毛婷[1] 杨敬辉[2] 杨晶东[3] 

机构地区：[1]上海第二工业大学人文与国际交流学院,上海210209 [2]上海第二工业大学经济与管理学院,上海210209 [3]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《兰州理工大学学报》2013年第5期89-93,共5页Journal of Lanzhou University of Technology

基　　金：国家自然科学基金(60874002)

摘　　要：根据自然语言语义特征提取、匹配的实时性和准确性要求,提出模糊聚类、单亲遗传搜索匹配算法相结合的新方法,通过对候选特征点进行模糊聚类处理,使其分布在高斯差分图像的灰度轮廓线边缘,利用单亲遗传算法找到满足约束条件全局最优语义特征,并把所有语义特征进行分类,给出分类依据.试验证明,此语义特征匹配算法在未知语境环境、语义特征频繁变化的环境具有很强的鲁棒性,能够在自然语言处理过程中实时准确识别段落中的语义特征.According to the requirement of real-time ability and validity in feature extraction and matching of semantic feature of natural language, a new algorithm of fuzzy clustering combined with partheno-genetic was presented. The candidate features were processed with fuzzy clustering, so that the extracted features were distributed around gray outline of Gaussian difference image. The globally optimal semantic features satisfying constraints could be found with partheno-genetic algorithm and all semantic features were classified and the classification basis was given at the same time. It was verified by experiment that this algorithm is strongly robust in an unknown language environment and as well in the environment where the semantic feature was frequently changing, so that the algorithm would be able to extract semantic features accurately and in real time from the paragraphs in the process of natural language processing.

关 键 词：模糊聚类 单亲遗传算法 特征匹配 语义特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]