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Configuring Analyzersedit
The third important index setting is the analysis
section, which is used
to configure existing analyzers or to create new custom analyzers
specific to your index.
In Analysis and Analyzers, we introduced some of the built-in analyzers, which are used to convert full-text strings into an inverted index, suitable for searching.
The standard
analyzer, which is the default analyzer
used for full-text fields, is a good choice for most Western languages.
It consists of the following:
-
The
standard
tokenizer, which splits the input text on word boundaries -
The
standard
token filter, which is intended to tidy up the tokens emitted by the tokenizer (but currently does nothing) -
The
lowercase
token filter, which converts all tokens into lowercase -
The
stop
token filter, which removes stopwords—common words that have little impact on search relevance, such asa
,the
,and
,is
.
By default, the stopwords filter is disabled. You can enable it by creating a
custom analyzer based on the standard
analyzer and setting the stopwords
parameter. Either provide a list of stopwords or tell it to use a predefined
stopwords list from a particular language.
In the following example, we create a new analyzer called the es_std
analyzer, which uses the predefined list of Spanish stopwords:
PUT /spanish_docs { "settings": { "analysis": { "analyzer": { "es_std": { "type": "standard", "stopwords": "_spanish_" } } } } }
The es_std
analyzer is not global—it exists only in the spanish_docs
index where we have defined it. To test it with the analyze
API, we must
specify the index name:
GET /spanish_docs/_analyze { "analyzer": "es_std", "text":"El veloz zorro marrón" }
The abbreviated results show that the Spanish stopword El
has been
removed correctly:
{ "tokens" : [ { "token" : "veloz", "position" : 2 }, { "token" : "zorro", "position" : 3 }, { "token" : "marrón", "position" : 4 } ] }