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。
Domain-dictionary based text representation can enhance the ability of text feature expression and reduce the feature dimensionality.
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.
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。
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.
中文文本的特征项抽取和表示是中文文本过滤基础。
Text feature extraction and representation is the fundamental operation for Chinese Text Filtering.
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.
使用向量空间模型来表示事件描述片段的特征，并分类计算特征词的重要度，最后对文本中的事件片段进行定位和分类。
This paper uses the Vector Space Model to express the features of event description segment and calculate the importance of feature words in different classes.
第一，提出一种有监督的潜在语义索引(SLSI)模型降维方法，用于文本分类任务中的特征表示。
The main contributions include: 1 a novel dimension reduction method, Supervised Latent Semantic Indexing SLSI, was proposed to represent documents for text classification tasks.
算法根据文本中汉字的特征建立文本表示矩阵和类别表示矩阵，并通过线性最小二乘算法形成分类矩阵。
Algorithm should build text expressing matrix and classification expressing matrix according to Chinese character features in text, and build classification matrix through using LLS Line...
算法根据文本中汉字的特征建立文本表示矩阵和类别表示矩阵，并通过线性最小二乘算法形成分类矩阵。
Algorithm should build text expressing matrix and classification expressing matrix according to Chinese character features in text, and build classification matrix through using LLS Line...
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