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.

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For Developers

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

Requirements

• Xcode Editor
• Git

Git

Install the latest version of Git.

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 NGram-Swift will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/TurkishDeasciifier-Swift.git

Open project with XCode

To import projects from Git with version control:

• XCode IDE, select Clone an Existing Project.

• In the Import window, paste github URL.

• Click Clone.

Result: The imported project is listed in the Project Explorer view and files are loaded.

Compile

From IDE

After being done with the downloading and opening project, select Build option from Product menu. After compilation process, user can run TurkishDeasciifier-Swift.

Detailed Description

• Asciifier
• Deasciifier

Using Asciifier

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

  Asciifier asciifier = SimpleAsciifier()
  Sentence sentence = Sentence("çocuk"")
  Sentence asciified = asciifier.asciify(sentence)

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.getAllWordsAsArrayList(), 1)
        ngram.calculateNGramProbabilities(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:

Sentence sentence = Sentence("cocuk")
Sentence deasciified = deasciifier.deasciify(sentence)

Output:

çocuk