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---------
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>
96 lines
2.8 KiB
Markdown
96 lines
2.8 KiB
Markdown
---
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mapped_pages:
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- https://www.elastic.co/guide/en/elasticsearch/plugins/current/analysis-nori-number.html
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---
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# nori_number token filter [analysis-nori-number]
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The `nori_number` token filter normalizes Korean numbers to regular Arabic decimal numbers in half-width characters.
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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.
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::::{note}
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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.
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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.
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::::
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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.
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* 영영칠 → 7
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* 일영영영 → 1000
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* 삼천2백2십삼 → 3223
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* 일조육백만오천일 → 1000006005001
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* ３.２천 → 3200
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* １.２만３４５.６７ → 12345.67
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* 4,647.100 → 4647.1
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* 15,7 → 157 (be aware of this weakness)
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For example:
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```console
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PUT nori_sample
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{
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"settings": {
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"index": {
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"analysis": {
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"analyzer": {
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"my_analyzer": {
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"tokenizer": "tokenizer_discard_puncuation_false",
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"filter": [
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"part_of_speech_stop_sp", "nori_number"
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]
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}
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},
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"tokenizer": {
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"tokenizer_discard_puncuation_false": {
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"type": "nori_tokenizer",
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"discard_punctuation": "false"
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}
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},
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"filter": {
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"part_of_speech_stop_sp": {
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"type": "nori_part_of_speech",
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"stoptags": ["SP"]
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}
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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 nori_sample/_analyze
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{
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"analyzer": "my_analyzer",
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"text": "십만이천오백과 ３.２천"
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}
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```
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Which results in:
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```console-result
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{
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"tokens" : [{
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"token" : "102500",
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"start_offset" : 0,
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"end_offset" : 6,
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"type" : "word",
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"position" : 0
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}, {
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"token" : "과",
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"start_offset" : 6,
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"end_offset" : 7,
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"type" : "word",
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"position" : 1
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}, {
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"token" : "3200",
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"start_offset" : 8,
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"end_offset" : 12,
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"type" : "word",
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"position" : 2
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}]
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}
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```
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