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[[proximity-matching]]
== Proximity Matching
translated by Yang
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## 模糊匹配
<!--
Standard full-text search with TF/IDF treats documents, or at least each field
within a document, as a big _bag of words_.((("proximity matching"))) The `match` query can tell us whether
that bag contains our search terms, but that is only part of the story.
It can't tell us anything about the relationship between words.
-->
一般的全文检索方式使用 TF/IDF 处理文本或者文本数据中的某个字段内容。将字面切分成很多字、词(word)建立索引,match查询用query中的term来匹配索引中的字、词。match查询提供了文档数据中是否包含我们需要的query中的单、词,但仅仅这样是不够的,它无法提供文本中的字词之间的关系。
<!--
Consider the difference between these sentences:
* Sue ate the alligator.
* The alligator ate Sue.
* Sue never goes anywhere without her alligator-skin purse.
A `match` query for `sue alligator` would match all three documents, but it
doesn't tell us whether the two words form part of the same idea, or even the same
paragraph.
-->
举个例子:
* 小苏吃了鳄鱼
* 鳄鱼吃了小苏
* 小苏去哪儿都带着的鳄鱼皮钱包
用`match`查询`小苏 鳄鱼`,这三句话都会被命中,但是`tf/idf`并不会告诉我们这两个词出现在同一句话里面还是在同一个段落中(仅仅提供这两个词在这段文本中的出现频率)
<!--
Understanding how words relate to each other is a complicated problem, and
we can't solve it by just using another type of query,
but we can at least find words that appear to be related because they appear
near each other or even right next to each other.
Each document may be much longer than the examples we have presented: `Sue`
and `alligator` may be separated by paragraphs of other text. Perhaps we still
want to return these documents in which the words are widely separated, but we
want to give documents in which the words are close together a higher relevance
score.
This is the province of _phrase matching_, or _proximity matching_.
-->
理解文本中词语之间的关系是一个很复杂的问题,而且这个问题通过更换query的表达方式是无法解决的。但是我们可以知道两个词语在文本中的距离远近,甚至是否相邻,这个信息似乎上能一定程度的表达这两个词比较相关。
一般的文本可能比我们举的例子长很多,正如我们提到的:`小苏`跟`鳄鱼`这两个词可能分布在文本的不同段落中。我们还是期望能找到这两个词分布均匀的文档,但是我们把这两个词距离比较近的文档赋予更好的相关性权重。
这就是段落匹配(_phrase matching_)或者模糊匹配(_proximity matching_)所做的事情。
<!--
[TIP]
==================================================
In this chapter, we are using the same example documents that we used for
the <<match-test-data,`match` query>>.
==================================================
-->
【**提示** 】
这一章,我们会用之之前在< match-test-data, `match` query >中使用的文档做例子。
- Introduction
- 入门
- 是什么
- 安装
- API
- 文档
- 索引
- 搜索
- 聚合
- 小结
- 分布式
- 结语
- 分布式集群
- 空集群
- 集群健康
- 添加索引
- 故障转移
- 横向扩展
- 更多扩展
- 应对故障
- 数据
- 文档
- 索引
- 获取
- 存在
- 更新
- 创建
- 删除
- 版本控制
- 局部更新
- Mget
- 批量
- 结语
- 分布式增删改查
- 路由
- 分片交互
- 新建、索引和删除
- 检索
- 局部更新
- 批量请求
- 批量格式
- 搜索
- 空搜索
- 多索引和多类型
- 分页
- 查询字符串
- 映射和分析
- 数据类型差异
- 确切值对决全文
- 倒排索引
- 分析
- 映射
- 复合类型
- 结构化查询
- 请求体查询
- 结构化查询
- 查询与过滤
- 重要的查询子句
- 过滤查询
- 验证查询
- 结语
- 排序
- 排序
- 字符串排序
- 相关性
- 字段数据
- 分布式搜索
- 查询阶段
- 取回阶段
- 搜索选项
- 扫描和滚屏
- 索引管理
- 创建删除
- 设置
- 配置分析器
- 自定义分析器
- 映射
- 根对象
- 元数据中的source字段
- 元数据中的all字段
- 元数据中的ID字段
- 动态映射
- 自定义动态映射
- 默认映射
- 重建索引
- 别名
- 深入分片
- 使文本可以被搜索
- 动态索引
- 近实时搜索
- 持久化变更
- 合并段
- 结构化搜索
- 查询准确值
- 组合过滤
- 查询多个准确值
- 包含,而不是相等
- 范围
- 处理 Null 值
- 缓存
- 过滤顺序
- 全文搜索
- 匹配查询
- 多词查询
- 组合查询
- 布尔匹配
- 增加子句
- 控制分析
- 关联失效
- 多字段搜索
- 多重查询字符串
- 单一查询字符串
- 最佳字段
- 最佳字段查询调优
- 多重匹配查询
- 最多字段查询
- 跨字段对象查询
- 以字段为中心查询
- 全字段查询
- 跨字段查询
- 精确查询
- 模糊匹配
- Phrase matching
- Slop
- Multi value fields
- Scoring
- Relevance
- Performance
- Shingles
- Partial_Matching
- Postcodes
- Prefix query
- Wildcard Regexp
- Match phrase prefix
- Index time
- Ngram intro
- Search as you type
- Compound words
- Relevance
- Scoring theory
- Practical scoring
- Query time boosting
- Query scoring
- Not quite not
- Ignoring TFIDF
- Function score query
- Popularity
- Boosting filtered subsets
- Random scoring
- Decay functions
- Pluggable similarities
- Conclusion
- Language intro
- Intro
- Using
- Configuring
- Language pitfalls
- One language per doc
- One language per field
- Mixed language fields
- Conclusion
- Identifying words
- Intro
- Standard analyzer
- Standard tokenizer
- ICU plugin
- ICU tokenizer
- Tidying text
- Token normalization
- Intro
- Lowercasing
- Removing diacritics
- Unicode world
- Case folding
- Character folding
- Sorting and collations
- Stemming
- Intro
- Algorithmic stemmers
- Dictionary stemmers
- Hunspell stemmer
- Choosing a stemmer
- Controlling stemming
- Stemming in situ
- Stopwords
- Intro
- Using stopwords
- Stopwords and performance
- Divide and conquer
- Phrase queries
- Common grams
- Relevance
- Synonyms
- Intro
- Using synonyms
- Synonym formats
- Expand contract
- Analysis chain
- Multi word synonyms
- Symbol synonyms
- Fuzzy matching
- Intro
- Fuzziness
- Fuzzy query
- Fuzzy match query
- Scoring fuzziness
- Phonetic matching
- Aggregations
- overview
- circuit breaker fd settings
- filtering
- facets
- docvalues
- eager
- breadth vs depth
- Conclusion
- concepts buckets
- basic example
- add metric
- nested bucket
- extra metrics
- bucket metric list
- histogram
- date histogram
- scope
- filtering
- sorting ordering
- approx intro
- cardinality
- percentiles
- sigterms intro
- sigterms
- fielddata
- analyzed vs not
- 地理坐标点
- 地理坐标点
- 通过地理坐标点过滤
- 地理坐标盒模型过滤器
- 地理距离过滤器
- 缓存地理位置过滤器
- 减少内存占用
- 按距离排序
- Geohashe
- Geohashe
- Geohashe映射
- Geohash单元过滤器
- 地理位置聚合
- 地理位置聚合
- 按距离聚合
- Geohash单元聚合器
- 范围(边界)聚合器
- 地理形状
- 地理形状
- 映射地理形状
- 索引地理形状
- 查询地理形状
- 在查询中使用已索引的形状
- 地理形状的过滤与缓存
- 关系
- 关系
- 应用级别的Join操作
- 扁平化你的数据
- Top hits
- Concurrency
- Concurrency solutions
- 嵌套
- 嵌套对象
- 嵌套映射
- 嵌套查询
- 嵌套排序
- 嵌套集合
- Parent Child
- Parent child
- Indexing parent child
- Has child
- Has parent
- Children agg
- Grandparents
- Practical considerations
- Scaling
- Shard
- Overallocation
- Kagillion shards
- Capacity planning
- Replica shards
- Multiple indices
- Index per timeframe
- Index templates
- Retiring data
- Index per user
- Shared index
- Faking it
- One big user
- Scale is not infinite
- Cluster Admin
- Marvel
- Health
- Node stats
- Other stats
- Deployment
- hardware
- other
- config
- dont touch
- heap
- file descriptors
- conclusion
- cluster settings
- Post Deployment
- dynamic settings
- logging
- indexing perf
- rolling restart
- backup
- restore
- conclusion