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模糊性评分。Scoring_fuzziness

_did-you-mean_ {ref}/search-suggesters-phrase.html[`phrase` suggester].
在模糊查询最初出现时很少能单独使用。他们更好的作为一个 ``bigger'' 场景的部分功能特性,如 _search-as-you-type_
{ref}/search-suggesters-completion.html[`完成` 建议]或
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这里预览时两个引用的背景加粗貌似有点奇怪

@Geolem
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Geolem commented Sep 28, 2016

LGTM

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@leo650
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leo650 commented Oct 19, 2016

LGTM

TIP: Fuzzy matching should not be used for scoring purposes--only to widen
the net of matching terms in case there are misspellings.
假设我们有1000个文档包含 ``Schwarzenegger`` ,只是一个文档的出现拼写错误 ``Schwarzeneger`` 。
根据 <<tfidf,term frequency/inverse document frequency>> 理论,这个拼写错误文档比拼写正确的相关度更高,因为它更少在文档中出现!
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因为它在更少的文档中出现!

解释:这里强调的是 TF-IDF 的 IDF值。


假设我们有1000个文档包含 ``Schwarzenegger`` ,只是一个文档的出现拼写错误 ``Schwarzeneger`` 。
根据 <<tfidf,term frequency/inverse document frequency>> 理论,这个拼写错误文档比拼写正确的相关度更高,因为它更少在文档中出现
根据 <<tfidf,term frequency/inverse document frequency>> 理论,这个拼写错误文档比拼写正确的相关度更高,因为错误拼写出现在更少在文档中
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更少在文档中 -> 更少的文档中

@node
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node commented Nov 21, 2016

LGTM

@medcl medcl merged commit f77159a into elasticsearch-cn:cn Nov 21, 2016
@node node added done and removed to be merge labels Nov 22, 2016
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5 participants