WRITING RULES FOR GRAMMALECTE

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

# 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.

The command to switch to the second pass is `[++]`.

In each pass, you can write as many rules as you need.

There are two kinds of rules:

* regex rules (triggered by a regular expression)
* token rules (triggered by a succession of tokens)

A regex rule is defined by:

* [optional] flags “LCR” for the regex word boundaries and case sensitiveness
* a regex pattern trigger
* a list of actions
* [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)

A token rules is defined by:

* rule name
* one or several lists of tokens (triggers)
* 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.

Each graph is defined within the second pass with the command:

        @@@@GRAPH: graph_name

A graph ends when another graph is defined or when is found the command:

        @@@@END_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 an URL
* Text transformation, modifying internally the checked text
* Disambiguation action, setting tags on a position
* Tagging


The rules file for your language must be named `rules.grx`.
The settings file must be named `config.ini`.

All these files are simple utf-8 text file.
UTF-8 is mandatory.


# Comments #

Lines beginning with `#` are comments.


# End of file #

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)__
            pattern
            <<- condition ->> error_suggestions  # message_error|http://awebsite.net...
            <<- 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 the order they are
written.

Conditions are optional, i.e.:

        <<- ~>> replacement


LCR flags means:

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

Left boundary (L):

>   `[`     word boundary

>   `<`     no word boundary

right boundary (R):

>   `]`     word boundary

>   `>`     no word boundary

Case sensitiveness (C):

>   `i`     case insensitive

>   `s`     case sensitive

>   `u`     uppercase allowed for lowercase characters

>>          i.e.:  "Word"  becomes  "W[oO][rR][dD]"

Examples:

        __[i]__  pattern
        __<s]__  pattern
        __[u>__  pattern
        __<s>__  pattern


User option activating/disactivating is possible with an option name placed
just after the LCR flags, i.e.:

        __[i]/option1__  pattern
        __[u]/option2__  pattern
        __[s>/option1__  pattern
        __<u>/option3__  pattern
        __<i>/option3__  pattern

Rules can be named:

        __[i]/option1(name1)__  pattern
        __[u]/option2(name2)__  pattern
        __[s>/option1(name3)__  pattern
        __<u>(name4)__          pattern
        __<i>(name5)__          pattern

Each rule name must be unique.


The LCR flags are also optional. If you don’t set these flags, the default LCR
flags will be:

        __[i]__

Example. Report “foo” in the text and suggest "bar":

        foo <<- ->> bar # Use bar instead of foo.

Example. Recognize and suggest missing hyphen and rewrite internally the text
with the hyphen:

        __[s]__ foo bar
            <<- ->> foo-bar # Missing hyphen.
            <<- ~>> foo-bar


## Simple-line or multi-line rules ##

Rules can be break to multiple lines by leading tabulators or spaces.
You should use 4 spaces.

Examples:

        __<s>__ pattern
            <<- condition ->> replacement
            # message
            <<- condition ->> suggestion # message
            <<- condition ~>> text_rewriting
            <<- =>> disambiguation

        __<s>__ pattern <<- condition ->> replacement # message


## Whitespaces at the border of patterns or suggestions ##

Example: Recognize double or more spaces and suggests a single space:

        __<s>__  "  +" <<- ->> " "      # Extra space(s).

Characters `"` protect spaces in the pattern and in the replacement text.


## Pattern groups and back references ##

It is usually useful to retrieve parts of the matched pattern. We simply use
parenthesis in pattern to get groups with back references.

Example. Suggest a word with correct quotation marks:

        \"(\w+)\" <<- ->> “\1”      # Correct quotation marks.

Example. Suggest the missing space after the !, ? or . signs:

        __<i]__ \b([?!.])([A-Z]+) <<- ->> \1 \2     # Missing space?

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


## Name definitions ##

Grammalecte supports name definitions to simplify the description of the
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:

        (ying) and yang <<- -1>> yin # Did you mean:

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


### Comparison ###

Rule A:

        ying and yang       <<- ->>     yin and yang        # Did you mean:

Rule B:

        (ying) and yang     <<- -1>>    yin                 # Did you mean:

With the rule A, the full pattern is underlined:

        ying and yang
        ^^^^^^^^^^^^^

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

WARNING: The replacing text must be shorter than the replaced text or have the
same length. Breaking this rule will misplace following error reports. You
have to ensure yourself the rules comply with this constraint, Grammalecte
won’t do it for you.

Specific commands for text rewriting:

`~>> *`

>   replace by whitespaces

`~>> @`

>   replace by arrobas, useful mostly at first pass, where it is advised to
>   check usage of punctuations and whitespaces.
>   @ are automatically removed at the beginning of the second pass.

You can use positioning with text rewriting actions.

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

You can also call Python expressions.

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


# Text preprocessing and multi-passes checking #

On each pass, Lightproof uses rules written in the text preprocessor to modify
internally the text before checking the text.

The text preprocessor is useful to simplify texts and write simplier checking
rules.

For example, sentences with the same grammar mistake:

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

Instead of writting 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.

Similarly to grammar rules, you can add conditions:

        \w+ly <<- morph(\0, "adverb") ->> *

You can also remove a group reference:

        these (\w+) (\w+) <<- morph(\1, "adjective") and morph(\2, "noun") -1>> *
        (am|are|is|were|was) (all) <<- -2>> *

With these rules, you get the following sentences:

        These  cats  are black.
        These cats are  black .
        These cats are            black.
        These         cats  are     black.
        These         cats are              black.

These grammar mistakes can be detected with one simple rule:

        these +(\w+) +are +(\w+s)
            <<- morph(\1, "noun") and morph(\2, "plural")
            -2>> _              # Adjectives are invariable.

Instead of replacing text with whitespaces, you can replace text with @.

        https?://\S+ ->> @

This is useful if at first pass you write rules to check successive whitespaces.
@ are automatically removed at the second pass.

You can also replace any text as you wish.

        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 is 3 commands for disambiguation.

`select(n, pattern)`

>   stores at position n only the POS tags of the word matching the pattern.

`exclude(n, pattern)`

>   stores at position n the POS tags of the word, except those matching the
    pattern.

`define(n, [definitions])`

>   stores at position n the POS tags in definitions (a list of strings).

Examples:

        =>> select(\1, "po:noun is:pl")
        =>> exclude(\1, "po:verb")
        =>> define(\1, ["po:adv"])
        =>> exclude(\1, "po:verb") and define(\2, ["po:adv"]) and select(\3, "po:adv")

Note: select, exclude and define ALWAYS return True.

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 catched

`word(-n)`

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

`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.

`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.


`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 the suggestions #

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


# 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

