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Comment:[doc] syntax update
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User & Date: olr on 2020-04-13 09:25:23
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2020-04-13
11:11
[fr] ajustements check-in: 6bf6e60197 user: olr tags: fr, trunk
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[doc] syntax update check-in: 0ac7e8df1f user: olr tags: doc, trunk
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Changes

Modified doc/syntax.txt from [c2a94b6c6f] to [97c9a7a212].

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# Writing rules for Grammalecte

Note: This documentation is a __draft__. Information may be obsolete or incomplete.


## Files required

The rules file for your language must be named `rules.grx` in the folder `gc_lang/<lang>/`.
The settings file must be named `config.ini`.

These files are simple UTF-8 text files.


## Principles

Grammalecte is a bi-passes grammar checker engine. On the first pass, the
engine checks the text paragraph by paragraph. On the second pass, the engine
check the text sentence by sentence.

You may alter how sentences are split by removing punctuation marks during the first pass.

................................................................................
* flags “LCR” for the regex word boundaries and case sensitiveness
* [optional] option name (the rule is active only if the option defined by user or config is active)
* [optional] rule name (named rules can be disabled by user or by config)
* [optional] priority number
* a regex pattern trigger
* a list of actions

A token rules is defined by:

* rule name
* [optional] priority number
* one or several lists of tokens
* a list of actions (the action is active only if the option defined by user or config is active)

Token rules must be defined within a graph.
................................................................................
There is no limit to the number of actions and the type of actions a rule can
launch. Each action has its own condition to be triggered.

There are several kinds of actions:

* Error warning, with a message, and optionally suggestions, and optionally a URL
* Text transformation, modifying internally the checked text
* [second pass only] Disambiguation action
* [second pass only] Tagging token
* [second pass only] Immunity rules


On the first pass, you can only write regex rules.
On the second pass, you can write regex rules and token rules. All tokens rules must be written within a graph.

## Syntax details



### Comments

Lines beginning with `#` are comments.


### End of parsing

With the command `#END` at the beginning of a line, the parser won’t go further.
Whatever is written after will be considered as comments.


## Regex rule syntax

    __LCR/option(rulename)!priority__
        pattern
            <<- condition ->> error_suggestions             # message_error|URL
            <<- condition ~>> text_rewriting
            <<- condition =>> commands_for_disambiguation
            ...
................................................................................

Patterns are written with the Python syntax for regular expressions:
http://docs.python.org/library/re.html

There can be one or several actions for each rule, executed following the order they are
written.

Optional:

* option
* rulename
* priority
* conditions
* URL


LCR flags means:

* L: Left boundary for the regex
* C: Case sensitiveness
* R: Right boundary for the regex

................................................................................
Example. Back reference in messages.

    (fooo) bar <<- ->> foo      # “\1” should be:


### Pattern matching

Repeating pattern matching of a single rule continues after the previous matching, so
instead of general multiword patterns, like

        (\w+) (\w+) <<- some_check(\1, \2) ->> \1, \2 # foo

use

        (\w+) <<- some_check(\1, word(1)) ->> \1, # foo


### Definitions

Grammalecte supports definitions to simplify the description of complex rules.




















































Example:

    DEF: name pattern




Usage in the rules:




    ({name}) (\w+) ->> "\1-\2"          # Missing hyphen?





### Multiple suggestions


Use `|` in the replacement text to add multiple suggestions:



Example. Foo, FOO, Bar and BAR suggestions for the input word "foo".


    foo <<- ->> Foo|FOO|Bar|BAR         # Did you mean:





### No suggestion


You can display message without making suggestions. For this purpose,
use a single character _ in the suggestion field.


Example. No suggestion.

    foobar <<- ->> _                    # Message
































































































### Positioning

Positioning is valid only for error creation and text rewriting.


By default, the full pattern will be underlined with blue. You can shorten the
underlined text area by specifying a back reference group of the pattern.
Instead of writing ->>, write -n>>  n being the number of a back reference
group. Actually,  ->>  is similar to  -0>>

Example:
................................................................................

With the rule B, only the first group is underlined:

    ying and yang
    ^^^^





















### Longer explanations with URLs

Warning messages can contain optional URL for longer explanations.

    your’s
        <<- ->> yours
            # Possessive pronoun:|http://en.wikipedia.org/wiki/Possessive_pronoun












### Text rewriting

Example. Replacing a string by another.

    Mr. [A-Z]\w+ <<- ~>> Mister
................................................................................

`~>> *`

>   Replace by whitespaces

`~>> @`

>   Replace with the at sign, useful mostly at first pass, where it is advised to
>   check usage of punctuations and whitespaces.
>   Successions of @ are automatically removed at the beginning of the second pass.

`~>> _`

>   Replace with underscores. Just a filler.
>   These characters won’t be removed at the beginning of the second pass.

................................................................................
    Mr(. [A-Z]\w+) <<- ~1>> *

You can also call Python expressions.

    __[s]__ Mr. ([a-z]\w+) <<- ~1>> =\1.upper()


### Text processing

The text processor is useful to simplify texts and write simpler checking
rules.

For example, sentences with the same grammar mistake:

    These “cats” are blacks.
    These cats are “blacks”.
    These cats are absolutely blacks.
    These stupid “cats” are all blacks.
    These unknown cats are as per usual blacks.

Instead of writing complex rules or several rules to find mistakes for all possible 
cases, you can use the text preprocessor to simplify the text.

To remove the chars “”, write:

    [“”] ~>> *

The * means: replace text by whitespaces.

................................................................................

    Mister <<- ~>> Mr
    (Mrs?)[.] <<- ~>> \1


### Disambiguation

When Grammalecte analyses a word with morph, before requesting the
POS tags to the dictionary, it checks if there is a stored marker for the
position where the word is. If there is a marker, Grammalecte uses the stored
data and don’t make request to the dictionary.

The disambiguation commands store POS tags at the position of a word.

There are 3 commands for disambiguation.
................................................................................

If select() and exclude() generate an empty list, no marker is set.

With define, you must set a list of POS tags. Example:

    define(\1, ["po:nom is:plur", "po:adj is:sing", "po:adv"])



### Conditions


Conditions are Python expressions, they must return a value, which will be
evaluated as boolean. You can use the usual Python syntax and libraries.

You can call pattern subgroups via \0, \1, \2…

Example:

    these (\w+)
        <<- \1 == "man" -1>> men        # Man is a singular noun. Use the plural form:

You can also apply functions to subgroups like:

    \1.startswith("a")
    \3.islower()
    re.search("pattern", \2)


### Standard functions

`word(n)`

>   catches the nth next word after the pattern (separated only by white spaces).
>   returns None if no word caught

`word(-n)`

>   catches the nth next word before the pattern (separated only by white spaces).
>   returns None if no word caught

`after(regex[, neg_regex])`

>   checks if the text after the pattern matches the regex.

`before(regex[, neg_regex])`

>   checks if the text before the pattern matches the regex.

`textarea(regex[, neg_regex])`

>    checks if the full text of the checked area (paragraph or sentence) matches the regex.

`morph(n, regex[, neg_regex][, no_word=False])`

>   checks if all tags of the word in group n match the regex.
>   if neg_regex = "*", returns True only if all morphologies match the regex.
>   if there is no word at position n, returns the value of no_word.

`morph0(n, regex[, neg_regex][, no_word=False])`

>   checks if all tags of the word in group n match the regex.
>   if neg_regex = "*", returns True only if all morphologies match the regex.
>   if there is no word at position n, returns the value of no_word.


`option(option_name)`

>   returns True if option_name is activated else False

Note: the analysis is done on the preprocessed text.


### Default variables

`sCountry`

>   It contains the current country locale of the checked paragraph.

    colour <<- sCountry == "US" ->> color       # Use American English spelling.



### Expressions in suggestion or replacement

Suggestions started by an equal sign are Python string expressions
extended with possible back references and named definitions:

Example:

    <<- ->> ='"' + \1.upper() + '"'         # With uppercase letters and quotation marks
    <<- ~>> =\1.upper()


## Token rules

Token rules must be defined within a graph.



### Tokens

Tokens can be defined in several ways:


* Value (meaning the text of the token). Examples: `word`, `<start>`, `<end>`, `,`.
* Lemma: `>lemma`.
* Regex: `~pattern`, `~pattern¬antipattern`.
* Regex on morphologies: `@pattern`, `@pattern¬antipattern`.
* Tags: `/tag`.
* Metatags: *NAME. Examples: `*WORD`, `*NUM`, `*SIGN`, etc.


Selection of tokens: `[token1|token2|>lemma1|>lemma2|~pattern1|@pattern1|…]`



Conditional token: `?token¿`


Conditional selection of token: `?[token1|token2|…]¿`




### Token references


Positive references are defined by a positive integer `>= 1`. Examples: \1, \2, \3, etc.
If there is at least one token set between parenthesis, these numbers refer to tokens between parenthesis, ignoring all others.
If there is no token between parenthesis, these numbers refer to tokens found in order defined by the rule triggered.


Negative references are defined by a negative integer `<= -1`. Examples: \-1, \-2, \-3, etc.
These numbers refer to the tokens beginning by the last one found by the rule triggered.

Examples:

    tokens:             alpha       beta        gamma       delta       epsilon
    positive refs:      1           2           3           4           5
    negative refs:      -5          -4          -3          -2          -1




    tokens:             alpha       (beta)      gamma       (delta)     epsilon
    positive refs:                  1                       2
    negative refs:      -5          -4          -3          -2          -1


    tokens:             alpha       (beta)      ?gamma¿     (delta)     epsilon
    positive refs:                  1                       2
    negative refs:      (-4/-5)     (-3/-4)     (-3/none)   -2          -1



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# Writing rules for Grammalecte #

Note: This documentation is a __draft__. Information may be obsolete or incomplete.


## FILES REQUIRED ##

The rules file for your language must be named `rules.grx` in the folder `gc_lang/<lang>/`.
The settings file must be named `config.ini`.

These files are simple UTF-8 text files.


## PRINCIPLES ##

Grammalecte is a bi-passes grammar checker engine. On the first pass, the
engine checks the text paragraph by paragraph. On the second pass, the engine
check the text sentence by sentence.

You may alter how sentences are split by removing punctuation marks during the first pass.

................................................................................
* flags “LCR” for the regex word boundaries and case sensitiveness
* [optional] option name (the rule is active only if the option defined by user or config is active)
* [optional] rule name (named rules can be disabled by user or by config)
* [optional] priority number
* a regex pattern trigger
* a list of actions

A token rule is defined by:

* rule name
* [optional] priority number
* one or several lists of tokens
* a list of actions (the action is active only if the option defined by user or config is active)

Token rules must be defined within a graph.
................................................................................
There is no limit to the number of actions and the type of actions a rule can
launch. Each action has its own condition to be triggered.

There are several kinds of actions:

* Error warning, with a message, and optionally suggestions, and optionally a URL
* Text transformation, modifying internally the checked text
* Disambiguation action
* [second pass only] Tagging token
* [second pass only] Immunity rules


On the first pass, you can only write regex rules.
On the second pass, you can write regex rules and token rules. All tokens rules must be written within a graph.

## REGEX RULE SYNTAX ##
















    __LCR/option(rulename)!priority__
        pattern
            <<- condition ->> error_suggestions             # message_error|URL
            <<- condition ~>> text_rewriting
            <<- condition =>> commands_for_disambiguation
            ...
................................................................................

Patterns are written with the Python syntax for regular expressions:
http://docs.python.org/library/re.html

There can be one or several actions for each rule, executed following the order they are
written.

Optional: option, rulename, priority, condition, URL








LCR flags means:

* L: Left boundary for the regex
* C: Case sensitiveness
* R: Right boundary for the regex

................................................................................
Example. Back reference in messages.

    (fooo) bar <<- ->> foo      # “\1” should be:


### Pattern matching

Repeating pattern matching of a single rule continues after the previous matching, so instead of general multiword patterns, like


        (\w+) (\w+) <<- some_check(\1, \2) ->> \1, \2 # foo

use

        (\w+) <<- some_check(\1, word(1)) ->> \1, # foo


## TOKEN RULES ##

Token rules must be defined within a graph.

### Token rules syntax

    __rulename!priority__
        list_of_tokens
        list_of_tokens
        list_of_tokens
        ...
            <<- /option/ condition ->> suggestions|URL
            <<- /option/ condition ~>> rewriting
            <<- /option/ condition =>> disambiguation
            <<- /option/ condition />> tagging
            <<- /option/ condition !>> <immunity>
            ...

        list_of_tokens
        ...
            <<- action1
            <<- action2
            ...

With token rules, for one rule name, you can define several blocks of list of tokens with different kinds of actions. Each block must be separated by an empty line.

Optional: priority, option, condition, URL

### Tokens

Tokens can be defined in several ways:

* Value (the text of the token). Examples: `word`, `<start>`, `<end>`, `,`.
* Lemma: `>lemma`.
* Regex: `~pattern`, `~pattern¬antipattern`.
* Regex on morphologies: `@pattern`, `@pattern¬antipattern`.
* Tags: `/tag`.
* Metatags: *NAME. Examples: `*WORD`, `*NUM`, `*SIGN`, etc.
* Jump over token: `<>`

Selection of tokens: `[value1|value2||>lemma|~pattern|@pattern|*META|/tag|…]`

Conditional token: `?token¿`

Conditional selection of token: `?[token1|token2|…]¿`

### Token references

Positive references are defined by a positive integer (> 0). Examples: `\1`, `\2`, `\3`, etc.
If there is at least one token set between parenthesis, these numbers refer to tokens between parenthesis, ignoring all others.
If there is no token between parenthesis, these numbers refer to tokens found in order defined by the rule triggered.

Negative references are defined by a negative integer (< 0). Examples: `\-1`, `\-2`, `\-3`, etc.
These numbers refer to the tokens beginning by the last one found by the rule triggered.

Examples:


    tokens:             alpha       beta        gamma       delta       epsilon
    positive refs:      1           2           3           4           5
    negative refs:      -5          -4          -3          -2          -1


    tokens:             alpha       (beta)      gamma       (delta)     epsilon
    positive refs:                  1                       2
    negative refs:      -5          -4          -3          -2          -1


    tokens:             alpha       (beta)      ?gamma¿     (delta)     epsilon
    positive refs:                  1                       2
    negative refs:      (-4/-5)     (-3/-4)     (-3/none)   -2          -1



## CONDITIONS ##


Conditions are Python expressions, they must return a value, which will be
evaluated as boolean. You can use the usual Python syntax and libraries.


With regex rules, you can call pattern subgroups via `\1`, `\2`… `\0` is the full pattern.


Example:

    these (\w+)
        <<- \1 == "man" -1>> men        # Man is a singular noun. Use the plural form:


You can also apply functions to subgroups like: `\1.startswith("a")` or `\3.islower()` or `re.search("pattern", \2)`.



With token rules, you can also call each token with their reference, like `\1`, `\2`... or `\-1`, `\-2`...

Example:


    foo [really|often|sometimes] bar
        <<- ->> \1 \-1                  # We say “foo bar”.


### Functions for regex rules

`word(n)`

>   Catches the nth next word after the pattern (separated only by white spaces).
>   Returns None if no word caught

`word(-n)`

>   Catches the nth next word before the pattern (separated only by white spaces).
>   Returns None if no word caught

`textarea(regex[, neg_regex])`

>    Checks if the full text of the checked area (paragraph or sentence) matches the regex.

`morph(n, regex[, neg_regex][, no_word=False])`

>   Checks if all tags of the word in group n match the regex.
>   If neg_regex = "*", returns True only if all morphologies match the regex.
>   If there is no word at position n, returns the value of no_word.

`analyse(n, regex[, neg_regex][, no_word=False])`

>   Checks if all tags of the word in group n match the regex.
>   If neg_regex = "*", returns True only if all morphologies match the regex.
>   If there is no word at position n, returns the value of no_word.

### Functions for token rules

`value(n, values_string)`

>   Analyses the value of the nth token.
>   The <values_string> contains values separated by the sign `|`.
>   Example: `"|foo|bar|"`

`morph(n, "regex", "neg_regex")`
`analyse(n, "regex", "neg_regex")`

>   Same action with morph() and morph0() for regex rules.

### Functions for regex and token rules

`__also__`

>   Returns True if the previous condition returned True.
>   Example: `<<- __also__ and condition2 ->>`

`__else__`

>   Returns False if the previous condition returned False.
>   Example: `<<- __else__ and condition2 ->>`

`option(option_name)`

>   Returns True if <option_name> is activated else False

Note: the analysis is done on the preprocessed text.

`after(regex[, neg_regex])`

>   Checks if the text after the pattern matches the regex.

`before(regex[, neg_regex])`

>   Checks if the text before the pattern matches the regex.

### Default variables

`sCountry`

>   It contains the current country locale of the checked paragraph.

    colour <<- sCountry == "US" ->> color       # Use American English spelling.


## ACTIONS ##

There are 5 kinds of actions:

1. Suggestions. The grammar checker suggests corrections.
2. Text processor. A internal process to modify the text internally. This is used to simplify grammar checking.
     * text rewriting
     * text deletion
     * token rewriting
     * token merging
     * token deletion
3. Disambiguation. Select, exclude or define morphologies of tokens.
4. Tagging. Add information on token.
5. Immunity. Prevent suggestions to be triggered.


### Positioning


Positioning is valid for suggestions, text processing, tagging and immunity.

By default, the full pattern will be underlined with blue. You can shorten the
underlined text area by specifying a back reference group of the pattern.
Instead of writing ->>, write -n>>  n being the number of a back reference
group. Actually,  ->>  is similar to  -0>>

Example:
................................................................................

With the rule B, only the first group is underlined:

    ying and yang
    ^^^^


### Suggestions

#### Multiple suggestions

Use `|` in the replacement text to add multiple suggestions:

Example. Foo, FOO, Bar and BAR suggestions for the input word "foo".

    foo <<- ->> Foo|FOO|Bar|BAR         # Did you mean:

#### No suggestion

You can display message without making suggestions. For this purpose,
use a single character _ in the suggestion field.

Example. No suggestion.

    foobar <<- ->> _                    # Message

#### Longer explanations with URLs

Warning messages can contain optional URL for longer explanations.

    your’s
        <<- ->> yours
            # Possessive pronoun:|http://en.wikipedia.org/wiki/Possessive_pronoun

#### Expressions in suggestion or replacement

Suggestions started by an equal sign are Python string expressions
extended with possible back references and named definitions:

Example:

    <<- ->> ='"' + \1.upper() + '"'         # With uppercase letters and quotation marks
    <<- ~>> =\1.upper()


### Text rewriting

Example. Replacing a string by another.

    Mr. [A-Z]\w+ <<- ~>> Mister
................................................................................

`~>> *`

>   Replace by whitespaces

`~>> @`

>   Replace with the at sign, useful mostly at first pass, where it is advised to check usage of punctuations and whitespaces.

>   Successions of @ are automatically removed at the beginning of the second pass.

`~>> _`

>   Replace with underscores. Just a filler.
>   These characters won’t be removed at the beginning of the second pass.

................................................................................
    Mr(. [A-Z]\w+) <<- ~1>> *

You can also call Python expressions.

    __[s]__ Mr. ([a-z]\w+) <<- ~1>> =\1.upper()




The text processor is useful to simplify texts and write simpler checking
rules.

For example, sentences with the same grammar mistake:

    These “cats” are blacks.
    These cats are “blacks”.
    These cats are absolutely blacks.
    These stupid “cats” are all blacks.
    These unknown cats are as per usual blacks.

Instead of writing complex rules or several rules to find mistakes for all possible cases, you can use the text preprocessor to simplify the text.


To remove the chars “”, write:

    [“”] ~>> *

The * means: replace text by whitespaces.

................................................................................

    Mister <<- ~>> Mr
    (Mrs?)[.] <<- ~>> \1


### Disambiguation

When Grammalecte analyses a word with `morph()`, before requesting the
POS tags to the dictionary, it checks if there is a stored marker for the
position where the word is. If there is a marker, Grammalecte uses the stored
data and don’t make request to the dictionary.

The disambiguation commands store POS tags at the position of a word.

There are 3 commands for disambiguation.
................................................................................

If select() and exclude() generate an empty list, no marker is set.

With define, you must set a list of POS tags. Example:

    define(\1, ["po:nom is:plur", "po:adj is:sing", "po:adv"])

### Tagging


**Only for token rules**


### Immunity
















































































**Only for token rules**



## OTHER COMMANDS ##

### Comments


Lines beginning with `#` are comments.







### End of parsing


With the command `#END` at the beginning of a line, the parser won’t go further.
Whatever is written after will be considered as comments.


### Definitions


Grammalecte supports definitions to simplify the description of complex rules.

Definition:


    DEF: name definition




Usage: `{name}` will be replaced by its definition




Example:




    DEF: word_3_letters     \w\w\w+  
    DEF: uppercase_token    ~^[A-Z]+$
    DEF: month_token        [January|February|March|April|May|June|July|August|September|October|November|december]




    ({word_3_letters}) (\w+) <<- condition ->> suggestion     # message|URL





    {uppercase_token} {month_token}
        <<- condition ->> message                             # message|URL