* [docs] Migrate docs from AsciiDoc to Markdown (#123507) * delete asciidoc files * add migrated files * fix errors * Disable docs tests * Clarify release notes page titles * Revert "Clarify release notes page titles" This reverts commit8be688648d
. * Comment out edternal URI images * Clean up query languages landing pages, link to conceptual docs * Add .md to url * Fixes inference processor nesting. --------- Co-authored-by: Liam Thompson <32779855+leemthompo@users.noreply.github.com> Co-authored-by: Liam Thompson <leemthompo@gmail.com> Co-authored-by: Martijn Laarman <Mpdreamz@gmail.com> Co-authored-by: István Zoltán Szabó <szabosteve@gmail.com> (cherry picked from commitb7e3a1e14b
) # Conflicts: # docs/build.gradle # docs/reference/migration/index.asciidoc # docs/reference/migration/migrate_9_0.asciidoc # docs/reference/release-notes.asciidoc # docs/reference/release-notes/9.0.0.asciidoc # docs/reference/release-notes/highlights.asciidoc * Fix build file * Really fix build file --------- Co-authored-by: Colleen McGinnis <colleen.j.mcginnis@gmail.com>
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nori_number token filter [analysis-nori-number]
The nori_number
token filter normalizes Korean numbers to regular Arabic decimal numbers in half-width characters.
Korean numbers are often written using a combination of Hangul and Arabic numbers with various kinds of punctuation. For example, 3.2천 means 3200. This filter does this kind of normalization and allows a search for 3200 to match 3.2천 in text, but can also be used to make range facets based on the normalized numbers and so on.
::::{note}
Notice that this analyzer uses a token composition scheme and relies on punctuation tokens being found in the token stream. Please make sure your nori_tokenizer
has discard_punctuation
set to false. In case punctuation characters, such as U+FF0E(.), is removed from the token stream, this filter would find input tokens 3 and 2천 and give outputs 3 and 2000 instead of 3200, which is likely not the intended result.
If you want to remove punctuation characters from your index that are not part of normalized numbers, add a stop
token filter with the punctuation you wish to remove after nori_number
in your analyzer chain.
::::
Below are some examples of normalizations this filter supports. The input is untokenized text and the result is the single term attribute emitted for the input.
- 영영칠 → 7
- 일영영영 → 1000
- 삼천2백2십삼 → 3223
- 일조육백만오천일 → 1000006005001
- 3.2천 → 3200
- 1.2만345.67 → 12345.67
- 4,647.100 → 4647.1
- 15,7 → 157 (be aware of this weakness)
For example:
PUT nori_sample
{
"settings": {
"index": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "tokenizer_discard_puncuation_false",
"filter": [
"part_of_speech_stop_sp", "nori_number"
]
}
},
"tokenizer": {
"tokenizer_discard_puncuation_false": {
"type": "nori_tokenizer",
"discard_punctuation": "false"
}
},
"filter": {
"part_of_speech_stop_sp": {
"type": "nori_part_of_speech",
"stoptags": ["SP"]
}
}
}
}
}
}
GET nori_sample/_analyze
{
"analyzer": "my_analyzer",
"text": "십만이천오백과 3.2천"
}
Which results in:
{
"tokens" : [{
"token" : "102500",
"start_offset" : 0,
"end_offset" : 6,
"type" : "word",
"position" : 0
}, {
"token" : "과",
"start_offset" : 6,
"end_offset" : 7,
"type" : "word",
"position" : 1
}, {
"token" : "3200",
"start_offset" : 8,
"end_offset" : 12,
"type" : "word",
"position" : 2
}]
}