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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.

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• Node.js 14 or higher
• Git

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.jshere.

Install thelatest version of Git.

npm install nlptoolkit-deasciifier

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

Steps for opening the cloned project:

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

• Asciifier
• Deasciifier

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

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

• SimpleDeasciifier

  The instantiation can be done as follows:

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

• NGramDeasciifier

  • To create an instance of this, both aFsmMorphologicalAnalyzer and aNGram 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, checkhere.

    • Loading from an existing model:

            let ngram = NGram("ngram.txt")

  For further details, please checkhere.

  • 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