English analyzer

The built-in english analyzer can be applied to a text field using the following command:

  1. PUT /english-index
  2. {
  3.   "mappings": {
  4.     "properties": {
  5.       "content": {
  6.         "type": "text",
  7.         "analyzer": "english"
  8.       }
  9.     }
  10.   }
  11. }

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Stem exclusion

You can use stem_exclusion with this language analyzer using the following command:

  1. PUT index_with_stem_exclusion_english_analyzer
  2. {
  3.   "settings": {
  4.     "analysis": {
  5.       "analyzer": {
  6.         "stem_exclusion_english_analyzer": {
  7.           "type": "english",
  8.           "stem_exclusion": ["authority", "authorization"]
  9.         }
  10.       }
  11.     }
  12.   }
  13. }

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English analyzer internals

The english analyzer is built using the following components:

  • Tokenizer: standard

  • Token filters:

    • stemmer (possessive_english)
    • lowercase
    • stop (English)
    • keyword
    • stemmer (English)

Custom English analyzer

You can create a custom English analyzer using the following command:

  1. PUT /english-index
  2. {
  3.   "settings": {
  4.     "analysis": {
  5.       "filter": {
  6.         "english_stop": {
  7.           "type": "stop",
  8.           "stopwords": "_english_"
  9.         },
  10.         "english_stemmer": {
  11.           "type": "stemmer",
  12.           "language": "english"
  13.         },
  14.         "english_keywords": {
  15.           "type": "keyword_marker",
  16.           "keywords": []
  17.         },
  18.         "english_possessive_stemmer": {
  19.           "type":       "stemmer",
  20.           "language":   "possessive_english"
  21.         }
  22.       },
  23.       "analyzer": {
  24.         "english_analyzer": {
  25.           "type": "custom",
  26.           "tokenizer": "standard",
  27.           "filter": [
  28.             "english_possessive_stemmer",
  29.             "lowercase",
  30.             "english_stop",
  31.             "english_keywords",
  32.             "english_stemmer"
  33.           ]
  34.         }
  35.       }
  36.     }
  37.   },
  38.   "mappings": {
  39.     "properties": {
  40.       "content": {
  41.         "type": "text",
  42.         "analyzer": "english_analyzer"
  43.       }
  44.     }
  45.   }
  46. }

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Generated tokens

Use the following request to examine the tokens generated using the analyzer:

  1. POST /english-index/_analyze
  2. {
  3.   "field": "content",
  4.   "text": "The students study in the USA and work at NASA. Their numbers are 123456."
  5. }

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The response contains the generated tokens:

  1. {
  2.   "tokens": [
  3.     {"token": "student","start_offset": 4,"end_offset": 12,"type": "<ALPHANUM>","position": 1},
  4.     {"token": "studi","start_offset": 13,"end_offset": 18,"type": "<ALPHANUM>","position": 2},
  5.     {"token": "usa","start_offset": 26,"end_offset": 29,"type": "<ALPHANUM>","position": 5},
  6.     {"token": "work","start_offset": 34,"end_offset": 38,"type": "<ALPHANUM>","position": 7},
  7.     {"token": "nasa","start_offset": 42,"end_offset": 46,"type": "<ALPHANUM>","position": 9},
  8.     {"token": "number","start_offset": 54,"end_offset": 61,"type": "<ALPHANUM>","position": 11},
  9.     {"token": "123456","start_offset": 66,"end_offset": 72,"type": "<NUM>","position": 13}
  10.   ]
  11. }