Turkish Spell Checker

This tool is a spelling checker for Modern Turkish. It detects spelling errors and corrects them appropriately, through its list of misspellings and matching to the Turkish dictionary.

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For Developers

You can also see Java, Python, Cython, Swift, C++, C, or C# repository.

Requirements

• Node.js 14 or higher
• Git

Node.js

To check if you have a compatible version of Node.js installed, use the following command:

node -v

You can find the latest version of Node.js here.

Git

Install the latest version of Git.

Npm Install

npm install nlptoolkit-spellchecker

Download Code

In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:

git clone <your-fork-git-link>

A directory called util will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/turkishspellchecker-js.git

Open project with Webstorm IDE

Steps for opening the cloned project:

• Start IDE
• Select File | Open from main menu
• Choose SepllChecker-Js file
• Select open as project option
• Couple of seconds, dependencies will be downloaded.

Detailed Description

• Creating SpellChecker
• Spell Correction

Creating SpellChecker

SpellChecker finds spelling errors and corrects them in Turkish. There are two types of spell checker available:

• SimpleSpellChecker

  • To instantiate this, a FsmMorphologicalAnalyzer is needed.

      let fsm = FsmMorphologicalAnalyzer();
      let spellChecker = SimpleSpellChecker(fsm);   
    

• NGramSpellChecker,

  • To create an instance of this, both a FsmMorphologicalAnalyzer and a NGram is required.

  • FsmMorphologicalAnalyzer can be instantiated as follows:

      let fsm = FsmMorphologicalAnalyzer();
    

  • NGram can be either trained from scratch or loaded from an existing model.

    • Training from scratch:

        let corpus = Corpus("corpus.txt"); 
        let ngram = NGram(corpus.getAllWords(), 1);
        ngram.calculateNGramProbabilities(new LaplaceSmoothing());
      

    There are many smoothing methods available. For other smoothing methods, check here.

    • Loading from an existing model:

        let ngram = NGram("ngram.txt");
      

  For further details, please check here.

  • Afterwards, NGramSpellChecker can be created as below:

      let spellChecker = NGramSpellChecker(fsm, ngram);
    

Spell Correction

Spell correction can be done as follows:

let sentence = new Sentence("Dıktor olaç yazdı");
let corrected = spellChecker.spellCheck(sentence);
Console.log(corrected);

Output:

Doktor ilaç yazdı

For Contibutors

package.json file

1. main and types are important when this package will be imported.

  "main": "dist/index.js",
  "types": "dist/index.d.ts",

2. Dependencies should be maximum (not only direct but also indirect references should also be given), everything directly in the code should be given here.

  "dependencies": {
    "nlptoolkit-corpus": "^1.0.12",
    "nlptoolkit-dictionary": "^1.0.14",
    "nlptoolkit-morphologicalanalysis": "^1.0.19",
    "nlptoolkit-xmlparser": "^1.0.7"
  }

tsconfig.json file

1. Compiler flags currently includes nodeNext for importing.

  "compilerOptions": {
    "outDir": "dist",
    "module": "nodeNext",
    "sourceMap": true,
    "noImplicitAny": true,
    "removeComments": false,
    "declaration": true,
  },

2. tests, node_modules and dist should be excluded.

  "exclude": [
    "tests",
    "node_modules",
    "dist"
  ]

index.ts file

1. Should include all ts classes.

export * from "./CategoryType"
export * from "./InterlingualDependencyType"
export * from "./InterlingualRelation"
export * from "./Literal"

Data files

1. Add data files to the project folder. Subprojects should include all data files of the parent projects.

Javascript files

1. Classes should be defined as exported.

export class JCN extends ICSimilarity{

2. Do not forget to comment each function.

    /**
     * Computes JCN wordnet similarity metric between two synsets.
     * @param synSet1 First synset
     * @param synSet2 Second synset
     * @return JCN wordnet similarity metric between two synsets
     */
    computeSimilarity(synSet1: SynSet, synSet2: SynSet): number {

3. Function names should follow caml case.

    setSynSetId(synSetId: string){

4. Write getter and setter methods.

    getRelation(index: number): Relation{
    setName(name: string){

5. Use standard javascript test style.

describe('SimilarityPathTest', function() {
    describe('SimilarityPathTest', function() {
        it('testComputeSimilarity', function() {
            let turkish = new WordNet();
            let similarityPath = new SimilarityPath(turkish);
            assert.strictEqual(32.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0656390"), turkish.getSynSetWithId("TUR10-0600460")));
            assert.strictEqual(13.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0412120"), turkish.getSynSetWithId("TUR10-0755370")));
            assert.strictEqual(13.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0195110"), turkish.getSynSetWithId("TUR10-0822980")));
        });
    });
});

6. Enumerated types should be declared with enum.

export enum CategoryType {
    MATHEMATICS, SPORT, MUSIC, SLANG, BOTANIC,
    PLURAL, MARINE, HISTORY, THEOLOGY, ZOOLOGY,
    METAPHOR, PSYCHOLOGY, ASTRONOMY, GEOGRAPHY, GRAMMAR,
    MILITARY, PHYSICS, PHILOSOPHY, MEDICAL, THEATER,
    ECONOMY, LAW, ANATOMY, GEOMETRY, BUSINESS,
    PEDAGOGY, TECHNOLOGY, LOGIC, LITERATURE, CINEMA,
    TELEVISION, ARCHITECTURE, TECHNICAL, SOCIOLOGY, BIOLOGY,
    CHEMISTRY, GEOLOGY, INFORMATICS, PHYSIOLOGY, METEOROLOGY,
    MINERALOGY
}

7. If there are multiple constructors for a class, define them as constructor1, constructor2, ..., then from the original constructor call these methods.

    constructor1(symbol: any){
    constructor2(symbol: any, multipleFile: MultipleFile) {
    constructor(symbol: any, multipleFile: MultipleFile = undefined) {
        if (multipleFile == undefined){
            this.constructor1(symbol);
        } else {
            this.constructor2(symbol, multipleFile);
        }
    }

8. Importing should be done via import method with referencing the node-modules.

import {Corpus} from "nlptoolkit-corpus/dist/Corpus";
import {Sentence} from "nlptoolkit-corpus/dist/Sentence";

9. Use xmlparser package for parsing xml files.

	var doc = new XmlDocument("test.xml")
	doc.parse()
	let root = doc.getFirstChild()
	let firstChild = root.getFirstChild()