基于N-Level VSM在Web信息检索中的研究

分析了传统向量空间检索模型在Web信息检索中的不足,给出了基于N-Level向量空间模型,这种模型是将一篇文档从逻辑上划分为N个相对独立的文本段,然后按照文本段的内容建立文本特征向量以及文本权值向量,在此基础上可以更加精确地定义特征值向量和相似度的计算方法,使之能比较好地适应文档集合的动态扩充。同时进行了两种模型算法时间的复杂度的比较分析。理论分析和实验结果表明,基于此模型实现的信息检索算法具有较快的查找速度和较高的查准率。

Study of Web Information Retrieval Based on N-Level Vector Space Model

Based on the analysis of the deficiency of the traditional vector space retrieval model,the N-level vector model is proposed.The N-level vector model partitions a document into N level text paragraphs.The text feature vectors and the text weight vectors are defined according to the text paragraphs' context.The calculation method of the feature vectors and the similarity are defined much more precisely such that the algorithm can adapt the dynamic extension of the document set.Meanwhile the time complexity of the algorithm is analyzed between the models.The theoretic analysis and the experimental results show that the new algorithm has higher precision and faster computation speed.