diff --git a/270_Fuzzy_matching/60_Phonetic_matching.asciidoc b/270_Fuzzy_matching/60_Phonetic_matching.asciidoc index 67bcb51ae..6e2fd59b6 100644 --- a/270_Fuzzy_matching/60_Phonetic_matching.asciidoc +++ b/270_Fuzzy_matching/60_Phonetic_matching.asciidoc @@ -1,28 +1,35 @@ [[phonetic-matching]] -=== 语音匹配 - -最后，在尝试任何其他匹配方法都无效后，我们可以求助于搜索发音相似的词，即使他们的拼写不同。 - - -存在一些将词转换成语音标识的算法。 -((("phonetic algorithms"))) http://en.wikipedia.org/wiki/Soundex[Soundex] 算法是这些算法的鼻祖， -而且大多数语音算法是 Soundex 的改进或者专业版本，例如 http://en.wikipedia.org/wiki/Metaphone[Metaphone] -和 http://en.wikipedia.org/wiki/Metaphone#Double_Metaphone[Double Metaphone] （扩展了除英语以外的其他语言的语音匹配）， -http://en.wikipedia.org/wiki/Caverphone[Caverphone] 算法匹配了新西兰的名称， -https://en.wikipedia.org/wiki/Daitch–Mokotoff_Soundex#Beider.E2.80.93Morse_Phonetic_Name_Matching_Algorithm[Beider-Morse] 算法吸收了 Soundex 算法为了更好的匹配德语和依地语名称， -http://de.wikipedia.org/wiki/K%C3%B6lner_Phonetik[Kölner Phonetik] 为了更好的处理德语词汇。 - - -值得一提的是，语音算法是相当简陋的，((("languages", "phonetic algorithms")))他们设计初衷针对的语言通常是英语或德语。这限制了他们的实用性。 -不过，为了某些明确的目标，并与其他技术相结合，语音匹配能够作为一个有用的工具。 - - -首先，你将需要从 -https://www.elastic.co/guide/en/elasticsearch/plugins/current/analysis-phonetic.html 获取在集群的每个节点安装((("Phonetic Analysis plugin")))语言分析器插件， -并且重启每个节点。 - - -然后，您可以创建一个使用语音语汇单元过滤器的自定义分析器，并尝试下面的方法： +=== Phonetic Matching + +In a last, desperate, attempt to match something, anything, we could resort to +searching for words that sound similar, ((("typoes and misspellings", "phonetic matching")))((("phonetic matching")))even if their spelling differs. + +Several algorithms exist for converting words into a phonetic +representation.((("phonetic algorithms"))) The http://en.wikipedia.org/wiki/Soundex[Soundex] algorithm is +the granddaddy of them all, and most other phonetic algorithms are +improvements or specializations of Soundex, such as +http://en.wikipedia.org/wiki/Metaphone[Metaphone] and +http://en.wikipedia.org/wiki/Metaphone#Double_Metaphone[Double Metaphone] +(which expands phonetic matching to languages other than English), +http://en.wikipedia.org/wiki/Caverphone[Caverphone] for matching names in New +Zealand, the +https://en.wikipedia.org/wiki/Daitch–Mokotoff_Soundex#Beider.E2.80.93Morse_Phonetic_Name_Matching_Algorithm[Beider-Morse] algorithm, which adopts the Soundex algorithm +for better matching of German and Yiddish names, and the +http://de.wikipedia.org/wiki/K%C3%B6lner_Phonetik[Kölner Phonetik] for better +handling of German words. + +The thing to take away from this list is that phonetic algorithms are fairly +crude, and ((("languages", "phonetic algorithms")))very specific to the languages they were designed for, usually +either English or German. This limits their usefulness. Still, for certain +purposes, and in combination with other techniques, phonetic matching can be a +useful tool. + +First, you will need to install ((("Phonetic Analysis plugin")))the Phonetic Analysis plug-in from +https://www.elastic.co/guide/en/elasticsearch/plugins/current/analysis-phonetic.html on every node +in the cluster, and restart each node. + +Then, you can create a custom analyzer that uses one of the +phonetic token filters ((("phonetic matching", "creating a phonetic analyzer")))and try it out: [source,json] ----------------------------------- @@ -46,11 +53,12 @@ PUT /my_index } } ----------------------------------- -<1> 首先，配置一个自定义 `phonetic` 语汇单元过滤器并使用 `double_metaphone` 编码器。 -<2> 然后在自定义分析器中使用自定义语汇单元过滤器。 +<1> First, configure a custom `phonetic` token filter that uses the + `double_metaphone` encoder. +<2> Then use the custom token filter in a custom analyzer. +Now we can test it with the `analyze` API: -现在我们可以通过 `analyze` API 来进行测试： [source,json] ----------------------------------- @@ -58,13 +66,13 @@ GET /my_index/_analyze?analyzer=dbl_metaphone Smith Smythe ----------------------------------- +Each of `Smith` and `Smythe` produce two tokens in the same position: `SM0` +and `XMT`. Running `John`, `Jon`, and `Johnnie` through the analyzer will all +produce the two tokens `JN` and `AN`, while `Jonathon` results in the tokens +`JN0N` and `ANTN`. -每个 `Smith` 和 `Smythe` 在同一位置产生两个语汇单元： `SM0` 和 `XMT` 。 -通过分析器播放 `John` ， `Jon` 和 `Johnnie` 将产生两个语汇单元 `JN` 和 `AN` ，而 `Jonathon` 产生语汇单元 `JN0N` 和 `ANTN` 。 - - -语音分析器可以像任何其他分析器一样使用。 首先映射一个字段来使用它，然后索引一些数据： - +The phonetic analyzer can be used just like any other analyzer. First map a +field to use it, and then index some data: [source,json] ----------------------------------- @@ -93,10 +101,9 @@ PUT /my_index/my_type/2 "name": "Jonnie Smythe" } ----------------------------------- -<1> `name.phonetic` 字段使用自定义 `dbl_metaphone` 分析器。 - +<1> The `name.phonetic` field uses the custom `dbl_metaphone` analyzer. -可以使用 `match` 查询来进行搜索： +The `match` query can be used for searching: [source,json] ----------------------------------- @@ -113,10 +120,15 @@ GET /my_index/my_type/_search } ----------------------------------- +This query returns both documents, demonstrating just how coarse phonetic +matching is. ((("phonetic matching", "purpose of"))) Scoring with a phonetic algorithm is pretty much worthless. The +purpose of phonetic matching is not to increase precision, but to increase +recall--to spread the net wide enough to catch any documents that might +possibly match.((("recall", "increasing with phonetic matching"))) + +It usually makes more sense to use phonetic algorithms when retrieving results +which will be consumed and post-processed by another computer, rather than by +human users. -这个查询返回全部两个文档，演示了如何进行简陋的语音匹配。 -((("phonetic matching", "purpose of"))) 用语音算法计算评分是没有价值的。 -语音匹配的目的不是为了提高精度，而是要提高召回率--以扩展足够的范围来捕获可能匹配的文档。 -通常是更有意义的使用语音算法是在检索到结果后，由另一台计算机进行消费和后续处理，而不是由人类用户直接使用。