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vector space model中文是什么意思

  • 向量空间模型

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  • 例句与用法
  • This thesis introduces the method to filtrate chinese document based on frequency statistics , the method developed from vector space model can get the correlative degree according to the user ' s interests and the effect is good
    本文结合智能搜索系统介绍了一种基于词频统计的文档过滤算法,该算法对传统的向量空间模型法作了改进,能够根据用户的兴趣爱好很好的对文档进行相关度的排序与过滤,取得了较好的效果。
  • Secondly , in order to find the solution to how to derive key words / entries from documents , this paper puts forward a matrix - weighted mining association rule algorithm based on the weighted mining algorithm and the weighted value of vector space model , vsm
    针对自动从文档中导出关键词词条之间的关联性问题,本文在深入研究加权挖掘算法和向量空间模型中权值特点的基础上,提出一种矩阵加权关联规则挖掘算法。
  • With steady structure , integrated meaning and statistical significance , key phrases ran overcome the limitation of vsm ( vector space model ) and nb ( naive - bayes ) , are fit for feature of text representation , and are propitious to improving effect of text categorization
    关键短语具有结构稳定、语义完整和较强统计意义的特点,能克服向量空间模型和贝叶斯假设的缺点,更适合作为文本表示的特征,有利于提高文本分类的效果。
  • First , this thesis has studied classic information retrieval for document from information retrieval theory , description by math , retrieval model and so on , designed and implemented a content retrieval experimental system based on vector space model
    本文首先针对xml文档的内容信息,从信息检索原理、数学描述、检索模型等方面较全面地研究了传统的文档信息检索技术,设计并实现了一个基于向量空间模型的内容检索试验系统。
  • Meeting the difference of text retrieval and sentence retrieval , a new method using integrating anaphora resolution , improved edit distance and vector space model is proposed in this paper . on factoid question type , the precision of answer sentence retrieval is up to 84 . 71 %
    针对文本检索和句子检索之间的区别,本文主要采用指代消解预处理,改进的编辑距离与向量空间模型相结合的方法,对factoid问题的答案句检索效果显著,准确率为84 . 71 % 。
  • The most popular vector space model is direct , concise and easy to be implemented . however , it can only express the user - interested keywords and cannot distinguish the different interests of users well . furthermore , the large amounts of keywords result in the jail of algorithm " s efficiency
    目前广为使用的向量空间模型直观、简明、实现方便,但只能表达用户感兴趣的关键词,而不能很好地区别用户兴趣之间的差异,并且关键词数量过多导致了算法效率降低。
  • Experiments are performed and results show : 1 the popular retrieval models the okapi s bm25 model and the smart s vector space model with length normalization do not perform well for document similarity search ; 2 the proposed model based on texttiling is effective and outperforms other models , including the cosine measure ; 3 the methods for the three components in the proposed model are validated to be appropriately employed
    我们通过实验验证了以下三点: 1 trec中的常用信息检索模型不能很好地解决文档相似搜索2我们提出的基于texttiling技术的模型是有效的,性能优于其他模型3我们提出的模型中所采用的方法是有效的,包括利用texttiling技术进行文本子主题分割,利用余弦公式来计算文本块之间的相似度,以及利用最优匹配方法来求解文档之间的总体相似度。
  • In this , content retrieval is achieved by content retrieval testing system based on vector space model , structure information is indexed by relation table through special numbering . we have implemented hybrid retrieval on content , structure and attribute of xml document
    其中, xml文档的内容信息检索通过基于向量空间模型的内容检索试验系统来完成,结构信息则通过特定编码,以关系表的方式进行索引,通过将关系数据库与传统信息检索技术的结合,实现了xml文档内容、结构、属性信息的综合检索。
  • Then it presents the design of information push service based on agent by using artificial intelligence technique . a brief introduction of each function module in this system and their internal transaction sequence are followed . the detail design and implement of key parts in each module is also given , which includes setting up user interest model with vector space model , searching information by using word segmenting and searching engine , filtering information using sorting algorithm , ordering information using pagerank algorithm
    本文首先分析了传统的信息“拉取”方式存在的主要问题以及推送技术的产生;然后结合人工智能领域的agent技术,提出了基于agent的信息推送服务的总体设计,并简要阐述了各功能模块的内部处理流程和思想;接着给出了各模块的详细设计与实现,主要包括:利用向量空间模型( vectorspacemodel )建立用户兴趣模型,通过分词并与搜索引擎协作实现信息检索,采用分类算法对已检索的信息进行过滤,用pagerank算法对信息排序。
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  • 百科解释
Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings.
详细百科解释
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Last modified time:Sat, 16 Aug 2025 00:29:56 GMT

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