--- mapped_pages: - https://www.elastic.co/guide/en/elasticsearch/plugins/current/analysis-nori-number.html --- # 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: ```console 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: ```console-result { "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 }] } ```