基于MM分词算法与SOM神经网络的分类方法研究

Research on classification algorithm based on maximum matching and SOM neural network

针对文本信息的分类问题,提出正向最大匹配分词算法与自组织映射神经网络融合算法(MMSOM)。利用正向最大匹配分词算法对文本信息进行自动提取,设定关键词信息规范框架,将规范化后的文本信息量化结果作为神经网络输入,结合文本分词结果,实现分类对象信息提取与分类的自动化。将该算法应用于藻类水华领域专家分类问题,分类结果表明了算法的可行性和有效性。

For the problem of text information classification, presented a fusion algorithm of positive maximum match segmentation algorithm combined with self-organizing neural network （MMSOM）. Extracted the text information automatically by using positive maximum match algorithm and set the specification frame of keywords information. Then the quantitative results of normalized text information have been used as the input vectors of the artificial neural network. Combined with the text segmentation results, realized the automation of the information extraction and classification to classification object. The algorithm has been applied to the classification problem of experts in algae bloom field, and the classification results show the feasibility and effectiveness.