NlpToolkit-SpellChecker-Cy 1.0.13

Turkish Spell Checker Library

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

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Requirements

• Python 3.7 or higher
• Git

Python

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

python -V

You can find the latest version of Python here.

Git

Install the latest version of Git.

Pip Install

pip3 install NlpToolkit-SpellChecker-Cy

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 SpellChecker will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/TurkishSpellChecker-Cy.git

Open project with Pycharm IDE

Steps for opening the cloned project:

• Start IDE
• Select File | Open from main menu
• Choose DataStructure-CY file
• Select open as project option
• Couple of seconds, project 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.

      fsm = FsmMorphologicalAnalyzer()
      spellChecker = SimpleSpellChecker(fsm)   
    

• NGramSpellChecker,

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

  • FsmMorphologicalAnalyzer can be instantiated as follows:

      fsm = FsmMorphologicalAnalyzer()
    

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

    • Training from scratch:

        corpus = Corpus("corpus.txt");
        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:

        ngram = NGram("ngram.txt")
      

  For further details, please check here.

  • Afterwards, NGramSpellChecker can be created as below:

      spellChecker = NGramSpellChecker(fsm, ngram)
    

Spell Correction

Spell correction can be done as follows:

sentence = Sentence("Dıktor olaç yazdı")
corrected = spellChecker.spellCheck(sentence)
print(corrected)

Output:

Doktor ilaç yazdı