rakiiibul / mcq-gen-nlp

This project basically generate MCQ questions from the text.

MCQ Generator Machine Learning (NLP) Notebook Project

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Overview:

This project aims to develop a machine learning-based system capable of generating multiple-choice questions (MCQs) from a given text corpus. The system utilizes natural language processing (NLP) techniques and machine learning algorithms to analyze the text and generate relevant MCQs automatically.

Features:

• Text Preprocessing: The notebook includes text preprocessing steps such as tokenization, removing stop words, and stemming or lemmatization to prepare the text data for further analysis.

• Feature Extraction: Utilizes techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings (e.g., Word2Vec, GloVe) to represent the text data in a format suitable for machine learning algorithms.

• Question Generation: Implements algorithms to automatically generate MCQs based on the processed text data. These algorithms may involve rule-based approaches, deep learning models, or a combination of both.

• User Interface: Optionally, a simple user interface can be developed to interact with the MCQ generation system, allowing users to input text data and receive generated MCQs in pdf file.

Usage:

1. Install Dependencies: Ensure that the required Python libraries such as NLTK, scikit-learn, TensorFlow, etc., are installed. You can install them using pip or conda.

2. Run the Notebook: Open the Jupyter notebook provided in this repository and run each cell sequentially. Make necessary adjustments to paths, parameters as needed.

Resources:

• NLTK Documentation
• scikit-learn Documentation
• TensorFlow Documentation
• Gensim Documentation

License:

This project is licensed under the Apache License.

Contributing:

Contributions to this project are welcome! Feel free to fork the repository, make improvements, and submit pull requests.

Acknowledgments:

Special thanks to Our Professor for supporting this project and providing valuable feedback and resources.

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