Text feature extraction and representation is the fundamental operation for Chinese Text Filtering.
中文文本的特征项抽取和表示是中文文本过滤基础。
Using latent semantic analysis to extract feature, the affect of synonymy and polysemy in text representation process is eliminated and the dimension of text vector is reduced.
利用潜在语义分析进行特征抽取，消除多义词和同义词在文本表示时造成的偏差，并实现文本向量的降维。
Domain-dictionary based text representation can enhance the ability of text feature expression and reduce the feature dimensionality.
基于领域词典的文本特征表示方法可以增强文本特征表示能力。
Computing method of weighted value for feature item based on text representation can determine extraction of text feature, which have influence on accuracy of the text clustering.
文本表示中特征项的权值计算方法决定了文本特征的提取，在很大程度上影响了文本聚类的准确率。
Computing method of weighted value for feature item based on text representation can determine extraction of text feature, which have influence on accuracy of the text clustering.
文本表示中特征项的权值计算方法决定了文本特征的提取，在很大程度上影响了文本聚类的准确率。
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