Grammer

A library to process noun (plural to singular and singular to plural), verb (gerund, present & past) and adjective (comparative, superlative) transformations.

This library is MIT licensed. So, it's free to use anytime, anywhere without any consent, because Open Source is love ❤️ .

Lets Get Started

1. Add dependency

Add this to your package's pubspec.yaml file:

dependencies:
  grammer: ^1.0.2

2. Import it

Now in your Dart code, you can use:

  import 'package:grammer/grammer.dart';

Usage

Adjective

var instance = Grammer('big');
instance.comparative(); // bigger
instance.superlative(); // biggest

Verb

 Grammer('rallied').conjugate('VBP'); // rally
 Grammer('fly').conjugate('VBD'); // flew
 Grammer('throw').conjugate('VBN'); // thrown
 Grammer('rally').conjugate('VBS'); // rallies
 Grammer('die').conjugate('VBG'); // dying

// or you can use the aliases
 Grammer('rallied').toPresent(); // rally
 Grammer('fly').toPast(); // flew
 Grammer('throw').toPastParticiple(); // thrown
 Grammer('rally').toPresentS(); // rallies
 Grammer('die').toGerund(); // dying

Noun

final grammerA = Grammer('bus');
final grammerB = Grammer('ellipses');
final grammerC = Grammer('money');

grammerA.isCountable(); // true
grammerB.isCountable(); // true
grammerC.isCountable(); // false

grammerA.isNotCountable(); // false
grammerB.isNotCountable(); // false
grammerC.isNotCountable(); // true

grammerA.isSingular(); // true
grammerB.isSingular(); // false
grammerC.isSingular(); // true

grammerA.isPlural(); // false
grammerB.isPlural(); // true
grammerC.isPlural(); // true

// note that uncountable words return true
// on both plural and singular checks

grammerA.toSingular(); // bus (no change)
grammerB.toSingular(); // ellipsis
grammerC.toSingular(); // money (no change)


grammerA.toPlural(); // [busses, buses]
grammerB.toPlural(); // ellipses (no change)
grammerC.toPlural(); // money (no change)

How does it work

Adjective

1. Checks against a dictionary of known irregularities (e.g. little/less/least)
2. Applies changes based on:
	* Number of syllables
	* word ending

Noun

1. Dictionary lookup (known irregularities e.g. octopus/octopi & uncountable words)
2. Identifies whether the word is plural or singular based on:
	* Dictionary
	* Machine learned regular expressions 
3. Applies transformation based on ending and word pattern (vowels, consonants and word endings)

Verb

1. Dictionary lookup (known irregularities + 4000 common verbs)
2. If the passed verb is identified as infinitive, it then applies regular expression transformations that are based on word endings, vowels and consonant phonetics.
3. Tries to trim character from the beginning of the verb, thus solving prefixes (e.g. undergoes, overthrown)
4. Tries to stem the word and get the infinitive form, then apply regular expression transformations.
5. Applies regular expressions.

How accurate is it?

First of all, unless you have a dictionary of all the words and verbs that exist in English, you can't really write a regular expression or an algorithm and expect to have a 100% success rate. English has been adopting words from a lot of different languages (French, Greek and Latin for example), and each one of these languages has its own rules of pluralization and singularization, let alone verb conjugation.

Even with dictionaries you'll have the problem of complex and made up words like maskedlocation, and you might have to add dictionaries for specialties (like medicine which does actually have its own dictionary).

However, I think what you'll find in this library is what can be achieved with the least amount of compromise.

I've used a set of rules (for detection/transformation) in combination with an exceptions list.

However, testing the library was more challenging than anticipated. If you have any case inaccuracy or false positives please submit an issue.

And of course, You can clone this repository, install grammer and test it (dart test) for yourself, and you'll see how it passes the 9900 tests successfully.

License

License: The MIT License (MIT) - Copyright (c) 2022 Kawaljeet Singh

Inspiration

(A migration of en-inflectors in Type-script.)