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For this, we turn to the\n-vector space model.\n+<1> 词 `fox` 在文档的内部 Lucene doc ID 为 `0` ，字段是 `text` 里的最终评分。\n+<2> 词 `fox` 在该文档 `text` 字段中只出现了一次。\n+<3> `fox` 在所有文档 `text` 字段索引的逆向文档频率。\n+<4> 该字段的字段长度规范值。\n \n+当然，查询通常不止一个词，所以需要一种合并多词权重的方式——向量空间（vector space model）。\n \n [[vector-space-model]]\n-==== Vector Space Model\n+==== 向量空间\n \n-The _vector space model_ provides a way of (((\"Vector Space Model\")))comparing a multiterm query\n-against a document. The output is a single score that represents how well the\n-document matches the query.  In order to do this, the model represents both the document\n-and the query as _vectors_.\n+_向量空间（vector space model）_ 提供一种比较多词查询的方式，(((\"Vector Space Model\")))单个评分代表文档与查询的匹配程度，为了做到这点，这个模型将文档和查询都以 _向量（vectors）_ 的形式表示：\n \n-A vector is really just a one-dimensional array containing numbers, for example:\n+向量实际上就是包含多个数的一维数组，例如：\n \n     [1,2,5,22,3,8]\n \n-In the vector space(((\"Term Frequency/Inverse Document Frequency  (TF/IDF) similarity algorithm\", \"in Vector Space Model\"))) model, each number in the vector is(((\"weight\", \"calculation of\", \"in Vector Space Model\"))) the _weight_ of a term,\n-as calculated with <<tfidf,term frequency/inverse document frequency>>.\n+在向量空间模型里，(((\"Term Frequency/Inverse Document Frequency  (TF/IDF) similarity algorithm\", \"in Vector Space Model\")))向量里的每个数字都代表一个词的 _权重_ ，与 <<tfidf,词频/逆向文档频率（term frequency/inverse document frequency）>> 计算方式类似。\n \n [TIP]\n ==================================================\n \n-While TF/IDF is the default way of calculating term weights for the vector\n-space model, it is not the only way.  Other models like Okapi-BM25 exist and\n-are available in Elasticsearch.  TF/IDF is the default because it is a\n-simple, efficient algorithm that produces high-quality search results and\n-has stood the test of time.\n+尽管 TF/IDF 是向量空间模型计算词权重的默认方式，但不是唯一方式。Elasticsearch 还有其他模型如 Okapi-BM25 。TF/IDF 是默认的因为它是个经检验过的简单又高效的算法，可以提供高质量的搜索结果。\n \n ==================================================\n \n-Imagine that we have a query for ``happy hippopotamus.''  A common word like\n-`happy` will have a low weight, while an uncommon term like `hippopotamus`\n-will have a high weight. Let's assume that `happy` has a weight of 2 and\n-`hippopotamus` has a weight of 5.  We can plot this simple two-dimensional\n-vector&#x2014;`[2,5]`&#x2014;as a line on a graph starting at point (0,0) and\n-ending at point (2,5), as shown in <<img-vector-query>>.\n+设想如果查询 “happy hippopotamus” ，常见词 `happy` 的权重较低，不常见词 `hippopotamus` 权重较高，假设 `happy` 的权重是 2 ， `hippopotamus` 的权重是 5 ，可以将这个二维向量—— `[2,5]` ——在坐标系下作条直线，线的起点是 (0,0) 终点是 (2,5) ，如图 <<img-vector-query>> 。\n \n [[img-vector-query]]\n-.A two-dimensional query vector for ``happy hippopotamus'' represented\n+。表示 “happy hippopotamus” 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