为了提高文本分类的准确性，研究并设计了一个基于潜在语义分析和支持向量机的多类文本分类模型。
A multiclass text categorization model based on latent semantic analysis and support vector machine is researched and designed to enhance the accuracy of categorization.
本文通过认真分析，认为在基于向量空间模型的分类方法中可以适当地借鉴基于语义的分类方法中的权重设置方法。
Therefore, by some analysis, the combination of classification based on vector space model and which based on semantic is one of the best solutions to this problem.
用骨架语片做特征项，用空间向量模型表示文本语义，用语片的出现频度做语片权重，用余弦法计算文本间语义相似度。
Computing the semantic similarity of sentences by the method of cosine, eigenvalue come from the skeleton semantic clip, and the semantics of sentence expressed the vector space model.
本文提出了两个新的图上关键字搜索算法，使用了现代信息检索技术中的向量空间模型和随机游走模型来解决以上缺陷，使得查询结果更具语义信息。
In this paper, two novel algorithms are introduced, which employ the vector space model and random walk model to address the drawbacks above and make the results more semantical.
对基于语义的向量空间模型的生成步骤做了详细的论述，对基于语义的和基于词形的两种分类系统的性能做了比较实验。
This paper gives the details of the building of the Vector Space Model based on the semantics. The experiments are carried on the system based on the semantics and the system based on the lemma.
对基于语义的向量空间模型的生成步骤做了详细的论述，对基于语义的和基于词形的两种分类系统的性能做了比较实验。
This paper gives the details of the building of the Vector Space Model based on the semantics. The experiments are carried on the system based on the semantics and the system based on the lemma.
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