{"action":"created","comment":{"url":"https://api.github.com/repos/elasticsearch-cn/elasticsearch-definitive-guide/pulls/comments/90770021","pull_request_review_id":11288144,"id":90770021,"diff_hunk":"@@ -1,92 +1,59 @@\n [[full-text-search]]\n-== Full-Text Search\n+== 全文搜索\n \n-Now that we have covered the simple case of searching for structured data,\n-it is time to (((\"full text search\")))explore _full-text search_: how to search within full-text fields in order to find the most relevant documents.\n+我们已经介绍了结构化搜索的简单应用示例，现在来探寻 _全文搜索（full-text search）_ ：怎样在全文字段中搜索到最相关的文档。\n \n-The two most important aspects of (((\"relevance\")))full-text search are as follows:\n+全文搜索两个最重要的方面是：(((\"relevance\")))\n \n-Relevance::\n+相关性（Relevance）::\n \n-    The ability to rank results by how relevant they are to\n-    the given query, whether relevance is calculated using\n-    TF/IDF (see <<relevance-intro>>), proximity to a geolocation,\n-    fuzzy similarity, or some other algorithm.\n+    它是评价查询与其结果间的相关程度，并根据这种相关程度对结果排名的一种能力，这种计算方式可以是 TF/IDF 方法（参见 <<relevance-intro, 相关性的介绍>>）、地理位置邻近、模糊相似，或其他的某些算法。\n \n-Analysis::\n+分析（Analysis）::\n \n-    The process of converting a block of text into distinct, normalized tokens\n-    (see <<analysis-intro>>) in order to (a) create an inverted index and\n-    (b) query the inverted index.\n+    它是将文本块转换为有区别的、规范化的 token 的一个过程，（参见 <<analysis-intro, 分析的介绍>>） 目的是为了（a）创建倒排索引以及（b）查询倒排索引。\n \n-As soon as we talk (((\"analysis\")))about either relevance or analysis, we are in the territory\n-of queries, rather than filters.\n+一旦谈论相关性或分析这两个方面的问题时，(((\"analysis\")))我们所处的语境是关于查询的而不是过滤。\n \n [[term-vs-full-text]]\n-=== Term-Based Versus Full-Text\n+=== 基于词项与基于全文\n \n-While all queries perform some sort of relevance calculation, not all queries\n-have an analysis phase.(((\"full text search\", \"term-based versus\")))(((\"term-based queries\"))) Besides specialized queries like the `bool` or\n-`function_score` queries, which don't operate on text at all, textual queries can\n-be broken down into two families:\n+所有查询会或多或少的执行相关度计算，但不是所有查询都有分析阶段。(((\"full text search\", \"term-based versus\")))(((\"term-based queries\")))与一些特殊查询（如 `bool` 或 `function_score` ）不同，我们完全不会对文本进行操作，而是将文本查询划分成两大家族：\n \n-Term-based queries::\n+基于词项的查询::\n +\n --\n \n-Queries like the `term` or `fuzzy` queries are low-level queries that have no\n-analysis phase.(((\"fuzzy queries\"))) They operate on a single term. A `term` query for the term\n-`Foo` looks for that _exact term_ in the inverted index and calculates the\n-TF/IDF relevance `_score` for each document that contains the term.\n+如 `term` 或 `fuzzy` 这样的底层查询不需要分析阶段，它们对单个词项进行操作。用 `term` 查询词项 `Foo` 只要在倒排索引中查找 _准确词项_ ，并且用 TF/IDF 算法为每个包含该词项的文档进行相关度评分 `_score` 。","path":"100_Full_Text_Search/00_Intro.asciidoc","position":52,"original_position":52,"commit_id":"aa4fee29857204184010d60da63f09550754e9e4","original_commit_id":"aa4fee29857204184010d60da63f09550754e9e4","user":{"login":"tangmisi","id":19425897,"avatar_url":"https://avatars.githubusercontent.com/u/19425897?v=3","gravatar_id":"","url":"https://api.github.com/users/tangmisi","html_url":"https://github.com/tangmisi","followers_url":"https://api.github.com/users/tangmisi/followers","following_url":"https://api.github.com/users/tangmisi/following{/other_user}","gists_url":"https://api.github.com/users/tangmisi/gists{/gist_id}","starred_url":"https://api.github.com/users/tangmisi/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tangmisi/subscriptions","organizations_url":"https://api.github.com/users/tangmisi/orgs","repos_url":"https://api.github.com/users/tangmisi/repos","events_url":"https://api.github.com/users/tangmisi/events{/privacy}","received_events_url":"https://api.github.com/users/tangmisi/received_events","type":"User","site_admin":false},"body":"进行相关度评分 `_score` 是否改成 计算相关度得分 `_score` 比较通顺点？可不改","created_at":"2016-12-04T03:45:19Z","updated_at":"2016-12-04T03:45:19Z","html_url":"https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/pull/372#discussion_r90770021","pull_request_url":"https://api.github.com/repos/elasticsearch-cn/elasticsearch-definitive-guide/pulls/372","_links":{"self":{"href":"https://api.github.com/repos/elasticsearch-cn/elasticsearch-definitive-guide/pulls/comments/90770021"},"html":{"href":"https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/pull/372#discussion_r90770021"},"pull_request":{"href":"https://api.github.com/repos/elasticsearch-cn/elasticsearch-definitive-guide/pulls/372"}}},"pull_request":{"url":"https://api.github.com/repos/elasticsearch-cn/elasticsearch-definitive-guide/pulls/372","id":95103474,"html_url":"https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/pull/372","diff_url":"https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/pull/372.diff","patch_url":"https://github.com/elasticsearch-cn/elasticsearch-definitive-guide/pull/372.patch","issue_url":"https://api.github.com/repos/elasticsearch-cn/elasticsearch-definitive-guide/issues/372","number":372,"state":"open","locked":false,"title":"chapter13_part1: /100_Full_Text_Search/00_Intro.asciidoc #Fix","user":{"login":"richardwei2008","id":1597335,"avatar_url":"https://avatars.githubusercontent.com/u/1597335?v=3","gravatar_id":"","url":"https://api.github.com/users/richardwei2008","html_url":"https://github.com/richardwei2008","followers_url":"https://api.github.com/users/richardwei2008/followers","following_url":"https://api.github.com/users/richardwei2008/following{/other_user}","gists_url":"https://api.github.com/users/richardwei2008/gists{/gist_id}","starred_url":"https://api.github.com/users/richardwei2008/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/richardwei2008/subscriptions","organizations_url":"https://api.github.com/users/richardwei2008/orgs","repos_url":"https://api.github.com/users/richardwei2008/repos","events_url":"https://api.github.com/users/richardwei2008/events{/privacy}","received_events_url":"https://api.github.com/users/richardwei2008/received_events","type":"User","site_admin":false},"body":"<!--\r\nThanks for the Pull Request!  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