【Key words】 text categorization; Vector Space Model; feature item weight; meaning information gain; weighted entropy;
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在这个模型中,有两个主要影响描述准确度的因素:一个是特征项的选择,一个是特征项的权重计算方式。
In this model, there are two factors affecting the description's precision: one is the choice of the feature words; another is the method of weight computing.
本文根据BP神经网络的函数逼近功能,针对文档特征项在文档中的权重,提出了一种基于BP神经网络的网络计算模型。
This paper presents a calculation model based on BP neural net to account the weight of a feature item in a document.
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.
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