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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Requirements

• C# 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 TurkishDeasciifier-CS will be created. Or you can use below link for exploring the code:

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

Open project with Rider IDE

To import projects from Git with version control:

• Open Rider IDE, select Get From Version Control.

• In the Import window, click URL tab and paste github URL.

• Click open as Project.

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 Solution option from Build menu. After compilation process, user can run TurkishDeasciifier-CS.

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 = new SimpleAsciifier();
  Sentence sentence = new Sentence("çocuk"");
  Sentence asciified = asciifier.Asciify(sentence);
  Console.WriteLine(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:

    FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer();
    Deasciifier deasciifier = new SimpleDeasciifier(fsm);
  

• NGramDeasciifier

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

  • FsmMorphologicalAnalyzer can be instantiated as follows:

      FsmMorphologicalAnalyzer fsm = new FsmMorphologicalAnalyzer();
    

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

    • Training from scratch:

        Corpus corpus = new Corpus("corpus.txt"); 
        NGram ngram = new NGram(corpus.getAllWordsAsArrayList(), 1);
        ngram.CalculateNGramProbabilities(new LaplaceSmoothing());
      

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

    • Loading from an existing model:

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

  For further details, please check here.

  • Afterwards, NGramDeasciifier can be created as below:

      Deasciifier deasciifier = new NGramDeasciifier(fsm, ngram);
    

A text can be deasciified as follows:

Sentence sentence = new Sentence("cocuk");
Sentence deasciified = deasciifier.Deasciify(sentence);
Console.WriteLine(deasciified);

Output:

çocuk