Automatic SpellChecking and Correction for Wolof Language

This is a fast spell checker tool implemented in Python that uses a trie node data structure and weighted Levenshtein distance algorithm for efficient and accurate spelling correction. The tool takes a text file as input, identifies misspelled words, and generates a new file with corrected text.

Features

Trie node data structure for efficient word lookup and suggestion generation.
Wolof Lexicon Lookup
Weighted Levenshtein distance algorithm to calculate the similarity between words.
Dynamic programming to optimize the Levenshtein distance calculation.
Generates a new file with corrected text, preserving the original file's formatting.

Installation

Clone the repository to your local machine or download the zip file
```
git clone https://github.com/TiDev00/Wolof_SpellChecker.git
```
Navigate to the project directory and Create a virtual environment (optional but recommended)
```
python -m venv env
```
Activate the virtual environment
- For Windows:
```
env\Scripts\activate
```
- For Linux/macOS:
```
source env/bin/activate
```
Install the required packages using pip:
```
pip install -r requirements.txt
```

Usage

Ensure that you have activated the virtual environment (if created).
Make sure in the input file each sentence is on a separate line
Run the spell checker tool with the following command:
```
python autocorrector.py /path/to/input_file.txt
```

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please create an issue or submit a pull request on the GitHub repository.

License

This project is licensed under the MIT License.

Acknowledgments

The tool is based on the concepts of trie node, weighted Levenshtein distance, lexicon lookup and dynamic programming.
Thanks to the open-source community for the packages used in this project.

Citation

If you use these resources in your research or project, please cite the paper :

@inproceedings{cisse-sadat-2023-automatic,
    title = "Automatic Spell Checker and Correction for Under-represented Spoken Languages: Case Study on {W}olof",
    author = "Ciss{\'e}, Thierno Ibrahima  and
      Sadat, Fatiha",
    booktitle = "Proceedings of the Fourth workshop on Resources for African Indigenous Languages (RAIL 2023)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.rail-1.1",
    pages = "1--10",
    abstract = "This paper presents a spell checker and correction tool specifically designed for Wolof, an under-represented spoken language in Africa. The proposed spell checker leverages a combination of a trie data structure, dynamic programming, and the weighted Levenshtein distance to generate suggestions for misspelled words. We created novel linguistic resources for Wolof, such as a lexicon and a corpus of misspelled words, using a semi-automatic approach that combines manual and automatic annotation methods. Despite the limited data available for the Wolof language, the spell checker{'}s performance showed a predictive accuracy of 98.31{\%} and a suggestion accuracy of 93.33{\%}.Our primary focus remains the revitalization and preservation of Wolof as an Indigenous and spoken language in Africa, providing our efforts to develop novel linguistic resources. This work represents a valuable contribution to the growth of computational tools and resources for the Wolof language and provides a strong foundation for future studies in the automatic spell checking and correction field.",
}

TiDev00 / Wolof_SpellChecker