chapter14_part6: /110_Multi_Field_Search/30_Most_fields.asciidoc (ela…

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* chapter14_part6: /110_Multi_Field_Search/30_Most_fields.asciidoc

初译

* 修改

对全文相关度提高精度的常用方式是为同一文本建立多种方式的索引 》提高全文相关性精度的常用方式是为同一文本建立多种方式的索引

* improve

110_Multi_Field_Search/30_Most_fields.asciidoc

@@ -1,61 +1,31 @@
 [[most-fields]]
-=== Most Fields
+=== 多数字段
 
-Full-text search is a battle between _recall_—returning all the
-documents that are ((("most fields queries")))((("multifield search", "most fields queries")))relevant--and _precision_—not returning irrelevant
-documents.  The goal is to present the user with the most relevant documents
-on the first page of results.
+全文搜索被称作是 _召回率（Recall）_ 与 _精确率（Precision）_ 的战场： _召回率_ ——返回结果中的所有文档都是相关的；((("most fields queries")))((("multifield search", "most fields queries"))) _精确率_ ——返回结果中没有不相关的文档。目的是在结果的第一页中为用户呈现最为相关的文档。
 
-To improve recall, we cast((("recall", "improving in full text searches"))) the net wide--we include not only
-documents that match the user's search terms exactly, but also
-documents that we believe to be pertinent to the query.  If a user searches
-for ``quick brown fox,'' a document that contains `fast foxes` may well be
-a reasonable result to return.
+为了提高召回率的效果，我们扩大搜索范围((("recall", "improving in full text searches")))——不仅返回与用户搜索词精确匹配的文档，还会返回我们认为与查询相关的所有文档。如果一个用户搜索 “quick brown box” ，一个包含词语 `fast foxes` 的文档被认为是非常合理的返回结果。
 
-If the only pertinent document that we have is the one containing `fast
-foxes`, it will appear at the top of the results list.  But of course, if
-we have 100 documents that contain the words `quick brown fox`, then the
-`fast foxes` document may be considered less relevant, and we would want to
-push it further down the list.  After including many potential matches, we
-need to ensure that the best ones rise to the top.
+如果包含词语 `fast foxes` 的文档是能找到的唯一相关文档，那么它会出现在结果列表的最上面，但是，如果有 100 个文档都出现了词语 `quick brown fox` ，那么这个包含词语 `fast foxes` 的文档当然会被认为是次相关的，它可能处于返回结果列表更下面的某个地方。当包含了很多潜在匹配之后，我们需要将最匹配的几个置于结果列表的顶部。
 
-A common technique for fine-tuning full-text relevance((("relevance", "fine-tuning full text relevance"))) is to index the same
-text in multiple ways, each of which provides a different relevance _signal_. The main field would contain terms in their broadest-matching form to match as
-many documents as possible.  For instance, we could do the following:
+提高全文相关性精度的常用方式是为同一文本建立多种方式的索引，((("relevance", "fine-tuning full text relevance")))每种方式都提供了一个不同的相关度信号 _signal_ 。主字段会包括最广匹配（broadest-matching）形式的词去尽可能的匹配更多的文档。举个例子，我们可以进行以下操作：
 
-*   Use a stemmer to index `jumps`, `jumping`, and `jumped` as their root
-    form: `jump`.  Then it doesn't matter if the user searches for
-    `jumped`; we could still match documents containing `jumping`.
+*   使用词干提取来索引 `jumps` 、 `jumping` 和 `jumped` 样的词，将 `jump` 作为它们的词根形式。这样即使用户搜索 `jumped` ，也还是能找到包含 `jumping` 的匹配的文档。
 
-*   Include synonyms like `jump`, `leap`, and `hop`.
+*   将同义词包括其中，如 `jump` 、 `leap` 和 `hop` 。
 
-*   Remove diacritics, or accents: for example, `ésta`, `está`, and `esta` would
-    all be indexed without accents as `esta`.
+*   移除变音或口音词：如 `ésta` 、 `está` 和 `esta` 都会以无变音形式 `esta` 来索引。
 
-However, if we have two documents, one of which contains `jumped` and the
-other `jumping`, the user would probably expect the first document to rank
-higher, as it contains exactly what was typed in.
+尽管如此，如果我们有两个文档，其中一个包含词 `jumped` ，另一个包含词 `jumping` ，用户很可能期望前者能排的更高，因为它正好与输入的搜索条件一致。
 
-We can achieve this by indexing the same text in other fields to provide more-precise matching.  One field may contain the unstemmed version, another the
-original word with diacritics, and a third might use _shingles_ to provide
-information about <<proximity-matching,word proximity>>. These other fields
-act as _signals_ that increase the relevance score of each matching document.
-The more fields that match, the better.
+为了达到目的，我们可以将相同的文本索引到其他字段从而提供更为精确的匹配。一个字段可能是为词干未提取过的版本，另一个字段可能是变音过的原始词，第三个可能使用 _shingles_ 提供 <<proximity-matching,词语相似性>> 信息。这些其他的字段作为提高每个文档的相关度评分的信号 _signals_ ，能匹配字段的越多越好。
 
-A document is included in the results list if it matches the broad-matching
-main field. If it also matches the _signal_ fields, it gets extra
-points and is pushed up the results list.
+一个文档如果与广度匹配的主字段相匹配，那么它会出现在结果列表中。如果文档同时又与 _signal_ 信号字段匹配，那么它会获得额外加分，系统会提升它在结果列表中的位置。
 
-We discuss synonyms, word proximity, partial-matching and other potential
-signals later in the book, but we will use the simple example of stemmed and
-unstemmed fields to illustrate this technique.
+我们会在本书稍后对同义词、词相似性、部分匹配以及其他潜在的信号进行讨论，但这里只使用词干已提取（stemmed）和未提取（unstemmed）的字段作为简单例子来说明这种技术。
 
-==== Multifield Mapping
-
-The first thing to do is to set up our ((("most fields queries", "multifield mapping")))((("mapping (types)", "multifield mapping")))field to be indexed twice: once in a
-stemmed form and once in an unstemmed form.  To do this, we will use 
-_multifields_, which we introduced in <<multi-fields>>:
+==== 多字段映射
 
+首先要做的事情就是对我们的字段索引两次：((("most fields queries", "multifield mapping")))((("mapping (types)", "multifield mapping")))一次使用词干模式以及一次非词干模式。为了做到这点，采用 _multifields_ 来实现，已经在 <<multi-fields,multifields>> 有所介绍：
 
 [source,js]
 --------------------------------------------------
@@ -84,11 +54,11 @@ PUT /my_index
 --------------------------------------------------
 // SENSE: 110_Multi_Field_Search/30_Most_fields.json
 
-<1> See <<relevance-is-broken>>.
-<2> The `title` field is stemmed by the `english` analyzer.
-<3> The `title.std` field uses the `standard` analyzer and so is not stemmed.
+<1> 参考 <<relevance-is-broken,被破坏的相关度>>.
+<2> `title` 字段使用 `english` 英语分析器来提取词干。
+<3> `title.std` 字段使用 `standard` 标准分析器，所以没有词干提取。
 
-Next we index some documents:
+接着索引一些文档：
 
 [source,js]
 --------------------------------------------------
@@ -100,7 +70,7 @@ PUT /my_index/my_type/2
 --------------------------------------------------
 // SENSE: 110_Multi_Field_Search/30_Most_fields.json
 
-Here is a simple `match` query on the `title` field for `jumping rabbits`:
+这里用一个简单 `match` 查询 `title` 标题字段是否包含 `jumping rabbits` （跳跃的兔子）：
 
 [source,js]
 --------------------------------------------------
@@ -115,9 +85,7 @@ GET /my_index/_search
 --------------------------------------------------
 // SENSE: 110_Multi_Field_Search/30_Most_fields.json
 
-This becomes a query for the two stemmed terms `jump` and `rabbit`, thanks to the
-`english` analyzer. The `title` field of both documents contains both of those
-terms, so both documents receive the same score:
+因为有了 `english` 分析器，这个查询是在查找以 `jump` 和 `rabbit` 这两个被提取词的文档。两个文档的 `title` 字段都同时包括这两个词，所以两个文档得到的评分也相同：
 
 [source,js]
 --------------------------------------------------
@@ -141,10 +109,7 @@ terms, so both documents receive the same score:
 }
 --------------------------------------------------
 
-If we were to query just the `title.std` field, then only document 2 would
-match.  However, if we were to query both fields and to _combine_ their scores
-by using the `bool` query, then both documents would match (thanks to the `title`
-field) and document 2 would score higher (thanks to the `title.std` field):
+如果只是查询 `title.std` 字段，那么只有文档 2 是匹配的。尽管如此，如果同时查询两个字段，然后使用 `bool` 查询将评分结果 _合并_ ，那么两个文档都是匹配的（ `title` 字段的作用），而且文档 2 的相关度评分更高（ `title.std` 字段的作用）：
 
 [source,js]
 --------------------------------------------------
@@ -161,9 +126,7 @@ GET /my_index/_search
 --------------------------------------------------
 // SENSE: 110_Multi_Field_Search/30_Most_fields.json
 
-<1>  We want to combine the scores from all matching fields, so we use the
-     `most_fields` type.  This causes the `multi_match` query to wrap the two
-     field-clauses in a `bool` query instead of a `dis_max` query.
+<1>  我们希望将所有匹配字段的评分合并起来，所以使用 `most_fields` 类型。这让 `multi_match` 查询用 `bool` 查询将两个字段语句包在里面，而不是使用 `dis_max` 查询。
 
 [source,js]
 --------------------------------------------------
@@ -186,16 +149,11 @@ GET /my_index/_search
   ]
 }
 --------------------------------------------------
-<1> Document 2 now scores much higher than document 1.
+<1> 文档 2 现在的评分要比文档 1 高。
 
-We are using the broad-matching `title` field to include as many documents as
-possible--to increase recall--but we use the `title.std` field as a
-_signal_ to push the most relevant results to the top.
+用广度匹配字段 `title` 包括尽可能多的文档——以提升召回率——同时又使用字段 `title.std` 作为 _信号_ 将相关度更高的文档置于结果顶部。
 
-The contribution of each field to the final score can be controlled by
-specifying custom `boost` values. For instance, we could boost the `title`
-field to make it the most important field, thus reducing the effect of any
-other signal fields:
+每个字段对于最终评分的贡献可以通过自定义值 `boost` 来控制。比如，使 `title` 字段更为重要，这样同时也降低了其他信号字段的作用：
 
 [source,js]
 --------------------------------------------------
@@ -212,6 +170,4 @@ GET /my_index/_search
 --------------------------------------------------
 // SENSE: 110_Multi_Field_Search/30_Most_fields.json
 
-<1> The `boost` value of `10` on the `title` field makes that field relatively
-    much more important than the `title.std` field.
-
+<1> `title` 字段的 `boost` 的值为 `10` 使它比 `title.std` 更重要。