中文文献的层次分类方法

现有的分类系统通常忽略类别体系的层次结构,在对文献进行分类时,往往很难区分类别相近的文献属于哪一类。本文基于向量空间模型,提出根据类别体系的层次结构,自顶向下,逐层分类的方法。其目的是提高分类精度;并根据概念词典,将同义词或下位概念映射到单一的概念词上,由这些概念词构成一个规模很小的特征集,以缩小特征向量空间的维数,从而减少分类系统的计算量。此外,通过对类别层次体系的分析,压缩特征向量,从另一方面减少分类系统的计算量。

Hierarchical Method for Chinese Document Classification

Existing statistical document classification systems often ignore the hierarchical structure of the pre defined topics. This makes it difficult to identify which category a document belongs to when the possible categories are somewhat similar. In this article, we propose a top down classification method according to the hierarchical structure of topics. The purpose is to improve precision and reduce computation of classification systems. Through a concept dictionary (thesaurus), we map the synonyms or lower level concepts in a document to a small set of concept words that are used as terms. This reduces the computational complexity from another aspect by reducing the dimension of the vector space.