Turkish Deasciifier

This tool is used to turn Turkish text written in ASCII characters, which do not include some letters of the Turkish alphabet, into correctly written text with the appropriate Turkish characters (such as ı, ş, and so forth). It can also do the opposite, turning Turkish input into ASCII text, for the purpose of processing.

Simple Web Interface

Asciifier Link 1 Asciifier Link 2

Deasciifier Link 1 Deasciifier Link 2

Video Lectures

For Developers

You can also see Python, Java, C++, C, Swift, Php, Cython 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-deasciifier

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/turkishdeasciifier-js.git

Open project with Webstorm IDE

Steps for opening the cloned project:

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

Detailed Description

• Asciifier
• Deasciifier

Using Asciifier

Asciifier converts text to a format containing only ASCII letters. This can be instantiated and used as follows:

  let asciifier = SimpleAsciifier()
  let sentence = Sentence("çocuk")
  let asciified = asciifier.asciify(sentence)
  console.log(asciified)

Output:

cocuk      

Using Deasciifier

Deasciifier converts text written with only ASCII letters to its correct form using corresponding letters in Turkish alphabet. There are two types of Deasciifier:

• SimpleDeasciifier

  The instantiation can be done as follows:

    let fsm = FsmMorphologicalAnalyzer()
    let deasciifier = SimpleDeasciifier(fsm)
  

• NGramDeasciifier

  • 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, NGramDeasciifier can be created as below:

      let deasciifier = NGramDeasciifier(fsm, ngram)
    

A text can be deasciified as follows:

let sentence = Sentence("cocuk")
let deasciified = deasciifier.deasciify(sentence)
console.log(deasciified)

Output:

çocuk

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()