Analysis: Add additional Analyzers, Tokenizers, and TokenFilters from Lucene
Add `irish` analyzer
Add `sorani` analyzer (Kurdish)
Add `classic` tokenizer: specific to english text and tries to recognize hostnames, companies, acronyms, etc.
Add `thai` tokenizer: segments thai text into words.
Add `classic` tokenfilter: cleans up acronyms and possessives from classic tokenizer
Add `apostrophe` tokenfilter: removes text after apostrophe and the apostrophe itself
Add `german_normalization` tokenfilter: umlaut/sharp S normalization
Add `hindi_normalization` tokenfilter: accounts for hindi spelling differences
Add `indic_normalization` tokenfilter: accounts for different unicode representations in Indian languages
Add `sorani_normalization` tokenfilter: normalizes kurdish text
Add `scandinavian_normalization` tokenfilter: normalizes Norwegian, Danish, Swedish text
Add `scandinavian_folding` tokenfilter: much more aggressive form of `scandinavian_normalization`
Add additional languages to stemmer tokenfilter: `galician`, `minimal_galician`, `irish`, `sorani`, `light_nynorsk`, `minimal_nynorsk`
Add support access to default Thai stopword set "_thai_"
Fix some bugs and broken links in documentation.
Closes #5935
2014-07-02 14:59:18 -04:00
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[[analysis-classic-tokenizer]]
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=== Classic Tokenizer
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2016-05-19 13:42:23 -04:00
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The `classic` tokenizer is a grammar based tokenizer that is good for English
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language documents. This tokenizer has heuristics for special treatment of
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acronyms, company names, email addresses, and internet host names. However,
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these rules don't always work, and the tokenizer doesn't work well for most
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languages other than English:
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* It splits words at most punctuation characters, removing punctuation. However, a
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dot that's not followed by whitespace is considered part of a token.
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* It splits words at hyphens, unless there's a number in the token, in which case
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the whole token is interpreted as a product number and is not split.
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* It recognizes email addresses and internet hostnames as one token.
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[float]
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=== Example output
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2019-09-09 13:38:14 -04:00
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[source,console]
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2016-05-19 13:42:23 -04:00
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---------------------------
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POST _analyze
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{
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"tokenizer": "classic",
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"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
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}
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---------------------------
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/////////////////////
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2019-09-06 09:22:08 -04:00
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[source,console-result]
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2016-05-19 13:42:23 -04:00
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----------------------------
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{
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"tokens": [
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{
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"token": "The",
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"start_offset": 0,
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"end_offset": 3,
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"type": "<ALPHANUM>",
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"position": 0
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},
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{
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"token": "2",
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"start_offset": 4,
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"end_offset": 5,
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"type": "<ALPHANUM>",
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"position": 1
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},
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{
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"token": "QUICK",
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"start_offset": 6,
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"end_offset": 11,
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"type": "<ALPHANUM>",
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"position": 2
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},
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{
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"token": "Brown",
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"start_offset": 12,
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"end_offset": 17,
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"type": "<ALPHANUM>",
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"position": 3
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},
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{
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"token": "Foxes",
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"start_offset": 18,
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"end_offset": 23,
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"type": "<ALPHANUM>",
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"position": 4
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},
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{
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"token": "jumped",
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"start_offset": 24,
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"end_offset": 30,
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"type": "<ALPHANUM>",
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"position": 5
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},
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{
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"token": "over",
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"start_offset": 31,
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"end_offset": 35,
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"type": "<ALPHANUM>",
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"position": 6
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},
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{
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"token": "the",
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"start_offset": 36,
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"end_offset": 39,
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"type": "<ALPHANUM>",
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"position": 7
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},
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{
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"token": "lazy",
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"start_offset": 40,
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"end_offset": 44,
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"type": "<ALPHANUM>",
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"position": 8
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},
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{
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"token": "dog's",
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"start_offset": 45,
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"end_offset": 50,
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"type": "<APOSTROPHE>",
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"position": 9
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},
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{
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"token": "bone",
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"start_offset": 51,
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"end_offset": 55,
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"type": "<ALPHANUM>",
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"position": 10
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}
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]
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}
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----------------------------
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/////////////////////
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The above sentence would produce the following terms:
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[source,text]
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---------------------------
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[ The, 2, QUICK, Brown, Foxes, jumped, over, the, lazy, dog's, bone ]
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---------------------------
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[float]
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=== Configuration
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The `classic` tokenizer accepts the following parameters:
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[horizontal]
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`max_token_length`::
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The maximum token length. If a token is seen that exceeds this length then
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it is split at `max_token_length` intervals. Defaults to `255`.
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[float]
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=== Example configuration
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In this example, we configure the `classic` tokenizer to have a
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`max_token_length` of 5 (for demonstration purposes):
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2019-09-09 13:38:14 -04:00
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[source,console]
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2016-05-19 13:42:23 -04:00
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----------------------------
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2019-01-18 03:34:11 -05:00
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PUT my_index
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2016-05-19 13:42:23 -04:00
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{
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"settings": {
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"analysis": {
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"analyzer": {
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"my_analyzer": {
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"tokenizer": "my_tokenizer"
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}
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},
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"tokenizer": {
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"my_tokenizer": {
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"type": "classic",
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"max_token_length": 5
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}
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}
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}
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}
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}
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POST my_index/_analyze
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{
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"analyzer": "my_analyzer",
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"text": "The 2 QUICK Brown-Foxes jumped over the lazy dog's bone."
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}
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----------------------------
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/////////////////////
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2019-09-06 09:22:08 -04:00
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[source,console-result]
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2016-05-19 13:42:23 -04:00
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----------------------------
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{
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"tokens": [
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{
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"token": "The",
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"start_offset": 0,
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"end_offset": 3,
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"type": "<ALPHANUM>",
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"position": 0
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},
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{
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"token": "2",
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"start_offset": 4,
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"end_offset": 5,
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"type": "<ALPHANUM>",
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"position": 1
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},
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{
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"token": "QUICK",
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"start_offset": 6,
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"end_offset": 11,
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"type": "<ALPHANUM>",
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"position": 2
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},
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{
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"token": "Brown",
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"start_offset": 12,
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"end_offset": 17,
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"type": "<ALPHANUM>",
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"position": 3
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},
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{
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"token": "Foxes",
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"start_offset": 18,
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"end_offset": 23,
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"type": "<ALPHANUM>",
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"position": 4
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},
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{
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"token": "over",
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"start_offset": 31,
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"end_offset": 35,
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"type": "<ALPHANUM>",
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"position": 6
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},
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{
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"token": "the",
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"start_offset": 36,
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"end_offset": 39,
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"type": "<ALPHANUM>",
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"position": 7
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},
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{
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"token": "lazy",
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"start_offset": 40,
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"end_offset": 44,
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"type": "<ALPHANUM>",
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"position": 8
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},
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{
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"token": "dog's",
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"start_offset": 45,
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"end_offset": 50,
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"type": "<APOSTROPHE>",
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"position": 9
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},
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{
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"token": "bone",
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"start_offset": 51,
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"end_offset": 55,
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"type": "<ALPHANUM>",
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"position": 10
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}
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]
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}
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----------------------------
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/////////////////////
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The above example produces the following terms:
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[source,text]
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---------------------------
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[ The, 2, QUICK, Brown, Foxes, jumpe, d, over, the, lazy, dog's, bone ]
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---------------------------
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