173 lines
4.2 KiB
Plaintext
173 lines
4.2 KiB
Plaintext
[[analysis-pattern-analyzer]]
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=== Pattern Analyzer
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The `pattern` analyzer uses a regular expression to split the text into terms.
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The regular expression should match the *token separators* not the tokens
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themselves. The regular expression defaults to `\W+` (or all non-word characters).
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[float]
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=== Definition
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It consists of:
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Tokenizer::
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* <<analysis-pattern-tokenizer,Pattern Tokenizer>>
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Token Filters::
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* <<analysis-lowercase-tokenfilter,Lower Case Token Filter>>
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* <<analysis-stop-tokenfilter,Stop Token Filter>> (disabled by default)
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[float]
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=== Example output
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[source,js]
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---------------------------
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POST _analyze
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{
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"analyzer": "pattern",
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"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
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}
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---------------------------
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// CONSOLE
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The above sentence would produce the following terms:
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[source,text]
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---------------------------
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[ the, 2, quick, brown, foxes, jumped, over, the, lazy, dog, s, bone ]
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---------------------------
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[float]
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=== Configuration
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The `pattern` analyzer accepts the following parameters:
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[horizontal]
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`pattern`::
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A http://docs.oracle.com/javase/8/docs/api/java/util/regex/Pattern.html[Java regular expression], defaults to `\W+`.
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`flags`::
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Java regular expression http://docs.oracle.com/javase/8/docs/api/java/util/regex/Pattern.html#field.summary[flags].
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lags should be pipe-separated, eg `"CASE_INSENSITIVE|COMMENTS"`.
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`lowercase`::
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Should terms be lowercased or not. Defaults to `true`.
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`max_token_length`::
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The maximum token length. If a token is seen that exceeds this length then
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it is split at `max_token_length` intervals. Defaults to `255`.
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`stopwords`::
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A pre-defined stop words list like `_english_` or an array containing a
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list of stop words. Defaults to `_none_`.
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`stopwords_path`::
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The path to a file containing stop words.
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See the <<analysis-stop-tokenfilter,Stop Token Filter>> for more information
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about stop word configuration.
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[float]
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=== Example configuration
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In this example, we configure the `pattern` analyzer to split email addresses
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on non-word characters or on underscores (`\W|_`), and to lower-case the result:
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[source,js]
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----------------------------
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PUT my_index
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"my_email_analyzer": {
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"type": "pattern",
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"pattern": "\\W|_", <1>
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"lowercase": true
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}
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}
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}
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}
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}
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GET _cluster/health?wait_for_status=yellow
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POST my_index/_analyze
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{
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"analyzer": "my_email_analyzer",
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"text": "John_Smith@foo-bar.com"
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}
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----------------------------
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// CONSOLE
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<1> The backslashes in the pattern need to be escaped when specifying the
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pattern as a JSON string.
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The above example produces the following terms:
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[source,text]
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---------------------------
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[ john, smith, foo, bar, com ]
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---------------------------
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[float]
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==== CamelCase tokenizer
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The following more complicated example splits CamelCase text into tokens:
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[source,js]
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--------------------------------------------------
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PUT my_index
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"camel": {
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"type": "pattern",
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"pattern": "([^\\p{L}\\d]+)|(?<=\\D)(?=\\d)|(?<=\\d)(?=\\D)|(?<=[\\p{L}&&[^\\p{Lu}]])(?=\\p{Lu})|(?<=\\p{Lu})(?=\\p{Lu}[\\p{L}&&[^\\p{Lu}]])"
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}
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}
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}
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}
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}
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GET _cluster/health?wait_for_status=yellow
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GET my_index/_analyze
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{
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"analyzer": "camel",
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"text": "MooseX::FTPClass2_beta"
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}
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--------------------------------------------------
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// CONSOLE
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The above example produces the following terms:
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[source,text]
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---------------------------
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[ moose, x, ftp, class, 2, beta ]
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---------------------------
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The regex above is easier to understand as:
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[source,js]
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--------------------------------------------------
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([^\p{L}\d]+) # swallow non letters and numbers,
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| (?<=\D)(?=\d) # or non-number followed by number,
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| (?<=\d)(?=\D) # or number followed by non-number,
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| (?<=[ \p{L} && [^\p{Lu}]]) # or lower case
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(?=\p{Lu}) # followed by upper case,
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| (?<=\p{Lu}) # or upper case
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(?=\p{Lu} # followed by upper case
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[\p{L}&&[^\p{Lu}]] # then lower case
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)
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--------------------------------------------------
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