MCQ Generator Machine Learning (NLP) Notebook Project
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.
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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.
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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.
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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.
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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.
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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.
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Run the Notebook: Open the Jupyter notebook provided in this repository and run each cell sequentially. Make necessary adjustments to paths, parameters as needed.
This project is licensed under the Apache License.
Contributions to this project are welcome! Feel free to fork the repository, make improvements, and submit pull requests.
Special thanks to Our Professor for supporting this project and providing valuable feedback and resources.