The kuromoji_tokenizer accepts the following settings:


The tokenization mode determines how the tokenizer handles compound and unknown words. It can be set to:


Normal segmentation, no decomposition for compounds. Example output:


Segmentation geared towards search. This includes a decompounding process for long nouns, also including the full compound token as a synonym. Example output:

関西, 関西国際空港, 国際, 空港

Extended mode outputs unigrams for unknown words. Example output:

関西, 国際, 空港
ア, ブ, ラ, カ, ダ, ブ, ラ
Whether punctuation should be discarded from the output. Defaults to true.

The Kuromoji tokenizer uses the MeCab-IPADIC dictionary by default. A user_dictionary may be appended to the default dictionary. The dictionary should have the following CSV format:

<text>,<token 1> ... <token n>,<reading 1> ... <reading n>,<part-of-speech tag>

As a demonstration of how the user dictionary can be used, save the following dictionary to $ES_HOME/config/userdict_ja.txt:

東京スカイツリー,東京 スカイツリー,トウキョウ スカイツリー,カスタム名詞

Additional expert user parameters nbest_cost and nbest_examples can be used to include additional tokens that most likely according to the statistical model. If both parameters are used, the largest number of both is applied.

The nbest_cost parameter specifies an additional Viterbi cost. The KuromojiTokenizer will include all tokens in Viterbi paths that are within the nbest_cost value of the best path.
The nbest_examples can be used to find a nbest_cost value based on examples. For example, a value of /箱根山-箱根/成田空港-成田/ indicates that in the texts, 箱根山 (Mt. Hakone) and 成田空港 (Narita Airport) we’d like a cost that gives is us 箱根 (Hakone) and 成田 (Narita).

Then create an analyzer as follows:

PUT kuromoji_sample
  "settings": {
    "index": {
      "analysis": {
        "tokenizer": {
          "kuromoji_user_dict": {
            "type": "kuromoji_tokenizer",
            "mode": "extended",
            "discard_punctuation": "false",
            "user_dictionary": "userdict_ja.txt"
        "analyzer": {
          "my_analyzer": {
            "type": "custom",
            "tokenizer": "kuromoji_user_dict"

POST kuromoji_sample/_analyze
  "analyzer": "my_analyzer",
  "text": "東京スカイツリー"

The above analyze request returns the following:

# Result
  "tokens" : [ {
    "token" : "東京",
    "start_offset" : 0,
    "end_offset" : 2,
    "type" : "word",
    "position" : 0
  }, {
    "token" : "スカイツリー",
    "start_offset" : 2,
    "end_offset" : 8,
    "type" : "word",
    "position" : 1
  } ]