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399 lines
10 KiB
Markdown
399 lines
10 KiB
Markdown
---
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slug: /en/sql-reference/functions/nlp-functions
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sidebar_position: 130
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sidebar_label: NLP (experimental)
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---
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# Natural Language Processing (NLP) Functions
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:::warning
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This is an experimental feature that is currently in development and is not ready for general use. It will change in unpredictable backwards-incompatible ways in future releases. Set `allow_experimental_nlp_functions = 1` to enable it.
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:::
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## detectCharset
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The `detectCharset` function detects the character set of the non-UTF8-encoded input string.
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*Syntax*
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``` sql
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detectCharset('text_to_be_analyzed')
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```
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*Arguments*
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- `text_to_be_analyzed` — A collection (or sentences) of strings to analyze. [String](../data-types/string.md#string).
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*Returned value*
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- A `String` containing the code of the detected character set
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*Examples*
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Query:
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```sql
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SELECT detectCharset('Ich bleibe für ein paar Tage.');
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```
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Result:
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```response
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┌─detectCharset('Ich bleibe für ein paar Tage.')─┐
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│ WINDOWS-1252 │
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└────────────────────────────────────────────────┘
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```
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## detectLanguage
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Detects the language of the UTF8-encoded input string. The function uses the [CLD2 library](https://github.com/CLD2Owners/cld2) for detection, and it returns the 2-letter ISO language code.
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The `detectLanguage` function works best when providing over 200 characters in the input string.
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*Syntax*
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``` sql
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detectLanguage('text_to_be_analyzed')
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```
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*Arguments*
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- `text_to_be_analyzed` — A collection (or sentences) of strings to analyze. [String](../data-types/string.md#string).
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*Returned value*
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- The 2-letter ISO code of the detected language
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Other possible results:
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- `un` = unknown, can not detect any language.
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- `other` = the detected language does not have 2 letter code.
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*Examples*
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Query:
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```sql
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SELECT detectLanguage('Je pense que je ne parviendrai jamais à parler français comme un natif. Where there’s a will, there’s a way.');
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```
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Result:
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```response
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fr
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```
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## detectLanguageMixed
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Similar to the `detectLanguage` function, but `detectLanguageMixed` returns a `Map` of 2-letter language codes that are mapped to the percentage of the certain language in the text.
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*Syntax*
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``` sql
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detectLanguageMixed('text_to_be_analyzed')
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```
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*Arguments*
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- `text_to_be_analyzed` — A collection (or sentences) of strings to analyze. [String](../data-types/string.md#string).
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*Returned value*
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- `Map(String, Float32)`: The keys are 2-letter ISO codes and the values are a percentage of text found for that language
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*Examples*
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Query:
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```sql
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SELECT detectLanguageMixed('二兎を追う者は一兎をも得ず二兎を追う者は一兎をも得ず A vaincre sans peril, on triomphe sans gloire.');
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```
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Result:
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```response
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┌─detectLanguageMixed()─┐
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│ {'ja':0.62,'fr':0.36 │
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└───────────────────────┘
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```
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## detectProgrammingLanguage
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Determines the programming language from the source code. Calculates all the unigrams and bigrams of commands in the source code.
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Then using a marked-up dictionary with weights of unigrams and bigrams of commands for various programming languages finds the biggest weight of the programming language and returns it.
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*Syntax*
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``` sql
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detectProgrammingLanguage('source_code')
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```
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*Arguments*
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- `source_code` — String representation of the source code to analyze. [String](../data-types/string.md#string).
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*Returned value*
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- Programming language. [String](../data-types/string.md).
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*Examples*
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Query:
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```sql
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SELECT detectProgrammingLanguage('#include <iostream>');
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```
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Result:
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```response
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┌─detectProgrammingLanguage('#include <iostream>')─┐
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│ C++ │
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└──────────────────────────────────────────────────┘
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```
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## detectLanguageUnknown
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Similar to the `detectLanguage` function, except the `detectLanguageUnknown` function works with non-UTF8-encoded strings. Prefer this version when your character set is UTF-16 or UTF-32.
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*Syntax*
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``` sql
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detectLanguageUnknown('text_to_be_analyzed')
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```
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*Arguments*
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- `text_to_be_analyzed` — A collection (or sentences) of strings to analyze. [String](../data-types/string.md#string).
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*Returned value*
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- The 2-letter ISO code of the detected language
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Other possible results:
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- `un` = unknown, can not detect any language.
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- `other` = the detected language does not have 2 letter code.
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*Examples*
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Query:
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```sql
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SELECT detectLanguageUnknown('Ich bleibe für ein paar Tage.');
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```
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Result:
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```response
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┌─detectLanguageUnknown('Ich bleibe für ein paar Tage.')─┐
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│ de │
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└────────────────────────────────────────────────────────┘
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```
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## detectTonality
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Determines the sentiment of text data. Uses a marked-up sentiment dictionary, in which each word has a tonality ranging from `-12` to `6`.
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For each text, it calculates the average sentiment value of its words and returns it in the range `[-1,1]`.
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:::note
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This function is limited in its current form. Currently it makes use of the embedded emotional dictionary at `/contrib/nlp-data/tonality_ru.zst` and only works for the Russian language.
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:::
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*Syntax*
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``` sql
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detectTonality(text)
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```
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*Arguments*
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- `text` — The text to be analyzed. [String](../data-types/string.md#string).
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*Returned value*
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- The average sentiment value of the words in `text`. [Float32](../data-types/float.md).
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*Examples*
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Query:
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```sql
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SELECT detectTonality('Шарик - хороший пёс'), -- Sharik is a good dog
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detectTonality('Шарик - пёс'), -- Sharik is a dog
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detectTonality('Шарик - плохой пёс'); -- Sharkik is a bad dog
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```
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Result:
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```response
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┌─detectTonality('Шарик - хороший пёс')─┬─detectTonality('Шарик - пёс')─┬─detectTonality('Шарик - плохой пёс')─┐
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│ 0.44445 │ 0 │ -0.3 │
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└───────────────────────────────────────┴───────────────────────────────┴──────────────────────────────────────┘
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```
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## lemmatize
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Performs lemmatization on a given word. Needs dictionaries to operate, which can be obtained [here](https://github.com/vpodpecan/lemmagen3/tree/master/src/lemmagen3/models).
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*Syntax*
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``` sql
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lemmatize('language', word)
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```
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*Arguments*
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- `language` — Language which rules will be applied. [String](../data-types/string.md#string).
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- `word` — Word that needs to be lemmatized. Must be lowercase. [String](../data-types/string.md#string).
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*Examples*
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Query:
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``` sql
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SELECT lemmatize('en', 'wolves');
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```
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Result:
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``` text
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┌─lemmatize("wolves")─┐
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│ "wolf" │
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└─────────────────────┘
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```
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*Configuration*
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This configuration specifies that the dictionary `en.bin` should be used for lemmatization of English (`en`) words. The `.bin` files can be downloaded from
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[here](https://github.com/vpodpecan/lemmagen3/tree/master/src/lemmagen3/models).
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``` xml
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<lemmatizers>
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<lemmatizer>
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<!-- highlight-start -->
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<lang>en</lang>
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<path>en.bin</path>
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<!-- highlight-end -->
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</lemmatizer>
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</lemmatizers>
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```
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## stem
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Performs stemming on a given word.
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*Syntax*
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``` sql
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stem('language', word)
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```
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*Arguments*
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- `language` — Language which rules will be applied. Use the two letter [ISO 639-1 code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes).
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- `word` — word that needs to be stemmed. Must be in lowercase. [String](../data-types/string.md#string).
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*Examples*
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Query:
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``` sql
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SELECT arrayMap(x -> stem('en', x), ['I', 'think', 'it', 'is', 'a', 'blessing', 'in', 'disguise']) as res;
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```
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Result:
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``` text
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┌─res────────────────────────────────────────────────┐
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│ ['I','think','it','is','a','bless','in','disguis'] │
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└────────────────────────────────────────────────────┘
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```
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*Supported languages for stem()*
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:::note
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The stem() function uses the [Snowball stemming](https://snowballstem.org/) library, see the Snowball website for updated languages etc.
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:::
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- Arabic
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- Armenian
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- Basque
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- Catalan
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- Danish
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- Dutch
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- English
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- Finnish
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- French
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- German
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- Greek
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- Hindi
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- Hungarian
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- Indonesian
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- Irish
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- Italian
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- Lithuanian
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- Nepali
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- Norwegian
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- Porter
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- Portuguese
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- Romanian
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- Russian
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- Serbian
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- Spanish
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- Swedish
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- Tamil
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- Turkish
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- Yiddish
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## synonyms
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Finds synonyms to a given word. There are two types of synonym extensions: `plain` and `wordnet`.
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With the `plain` extension type we need to provide a path to a simple text file, where each line corresponds to a certain synonym set. Words in this line must be separated with space or tab characters.
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With the `wordnet` extension type we need to provide a path to a directory with WordNet thesaurus in it. Thesaurus must contain a WordNet sense index.
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*Syntax*
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``` sql
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synonyms('extension_name', word)
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```
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*Arguments*
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- `extension_name` — Name of the extension in which search will be performed. [String](../data-types/string.md#string).
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- `word` — Word that will be searched in extension. [String](../data-types/string.md#string).
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*Examples*
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Query:
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``` sql
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SELECT synonyms('list', 'important');
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```
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Result:
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``` text
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┌─synonyms('list', 'important')────────────┐
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│ ['important','big','critical','crucial'] │
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└──────────────────────────────────────────┘
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```
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*Configuration*
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``` xml
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<synonyms_extensions>
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<extension>
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<name>en</name>
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<type>plain</type>
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<path>en.txt</path>
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</extension>
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<extension>
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<name>en</name>
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<type>wordnet</type>
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<path>en/</path>
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</extension>
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</synonyms_extensions>
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``` |