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- Generalized Vector Space Model 广义向量空间模型
- Compared with the vector space model, the probability model is more effective on describing a users interests. 对比实验表明,概率模型比矢量空间模型更好地表达了用户的兴趣和变化。
- Through the vector space model, text clustering, genetic algorithms to try a new method summary of the text. 通过空间向量模型、文本聚类、遗传算法等成熟的技术尝试一种新的文本摘要方法。
- We propose a text vector space model (VSM) based on suffix tree and implement a text categorizing system on the model. 摘要本文在面向网络内容分析的前提下，提出了一种基于后缀树的文本向量空间模型(VSM)，并在此模型之上实现了文本分类系统。
- This paper introduces a text clustering method based on VSM (vector space model). 摘要研究了一种基于向量空间模型的文档聚类方法。
- In the paper, a method is proposed based on vector space model making use of the structure to effectively retrieve HTML document. ? 应用了向量空间信息检索方法,扩展了传统的信息检索,利用HTML文档结构提高了在WWW环境下的检索效率。
- Aimed at the problems of document automatic classification,a classification method is proposed based on fuzzy vector space model and RBF network. 针对文本自动分类问题,提出了一种基于模糊向量空间模型和径向基函数网络的分类方法。
- The algorithm utilized VSM (vector space model) to represent users' interests and resources, calculated recommended degree by cosine similarity. 该算法采用矢量空间模型作为用户兴趣和资源描述模型，使用余弦相似度计算资源推荐度；
- In view of the defect,a method of Chinese text categorization based on the word vector space model is presented in this paper. 针对这一不足,该文提出基于词向量空间模型的文本分类方法。
- In information retrieval systems based on the vector space model, the TF-IDF scheme is widely used to characterize documents. 摘要在基于向量空间模型的信息检索系统中，TF-IDF算法被广泛的应用在基于关键字的信息检索中。
- The principal of Vector Space Model is presented and comprehensively studied, and we analyze the performance criterias of document searching system. 摘要文章介绍了向量空间模型的基本原理，分析了文本检索系统常用的性能评估标准。
- The new method establishes vector space model of term weight according to the theory of latent semantic index, and may eliminate disadvantageous factors. 该方法应用lsi理论来建立文本集的向量空间模型;在词条的权重中引入了语义关系;消减了原词条矩阵中包含的"噪声"因素;从而更加突出了词和文本之间的语义关系.
- Our automatic multi-document summarization system combine a series of NLP techniques, such as text segmentation, text clustering and Vector Space Model. 系统综合了一些自然语言处理技术，包括文本分段、文本段聚类、向量空间模型的相似度计算等。
- The traditional VSM(Vector Space Model) system often selects words as the feature,and one of its shortness is the lack of semantic information in document representation. 传统的向量空间过滤模型通常是提取字、词、短语等作为特征项,这样做的缺点是没有考虑文本的语义信息。
- Most Web page classification methods are based on Vector Space Model(VSM), but it is not suitable for large scale application background with bad computation complexity. 1概述如何利用因特网海量信息资源找到有用的信息,是网页分类关注的重点。
- The feature selection of traditional text categorization takes the word as unit,and establish vector space model to express all the documents according to the features weighting. 传统的文本分类的特征选择都是以词为单位,根据计算特征词的权重建立向量空间模型,进而表示所有文档。
- This paper applies the improved information gain method to feature selection,constructs the spam feature vector according to Vector Space Model,and uses SVM to spam filtering. 利用改进的信息增益特征选择的方法来提取特征词,基于向量空间模型构造邮件的特征向量,最后用支持向量机算法对邮件进行过滤。
- Vector Space Model(VSM) which is testified an better text denotation model is used in this paper.Criteria based on geometrical distance and post-test probability are introduced . 文本表示方面采用近年来应用较多且效果较好的向量空间模型(VSM),给出了基于几何距离的可分性判据和基于后验概率的可分性判据。
- This method is based on RST (rhetorical structure theory) analysis, thematic progression analysis and other linguistic methods. Vector space model and other statistical methods are also employed to inhance its robustness. 此方法主要基于 RST( rhetorical structure theory)分析、主位模式分析等多种语言学方法 ;还利用了向量空间模型等统计方法 .
- In order tofindthe solutionto derive key words/entries fromdocuments,this paper puts forward anew matrix-weighted mining association rule algorithm based on the weighted mining algorithm and theweighted value of vector space model. 针对自动从文档中导出关键词/词条之间的关联性问题,在研究加权挖掘算法和向量空间模型中权值特点的基础上,提出了一种新的矩阵加权关联规则挖掘算法。
