NLP-Tutorial
A comprehensive tutorial series covering fundamental concepts and techniques in Natural Language Processing (NLP) using Python. This project is designed to guide learners through various NLP tasks, providing practical examples and code implementations.
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README
NLP Tutorial
A comprehensive tutorial series covering fundamental concepts and techniques in Natural Language Processing (NLP) using Python. This project is designed to guide learners through various NLP tasks, providing practical examples and code implementations.
Table of Contents
- Project Structure
- Installation
- Usage
- Chapter Overview
- Chapter 6: Word Embedding
- Contributing
- License
- Contact
Project Structure
The repository is organized into the following chapters:
- Regex Tutorial NLP: Introduction to regular expressions in NLP.
- Tokenization with SpaCy: Techniques for tokenizing text using the SpaCy library.
- Stemming and Lemmatization: Understanding and implementing stemming and lemmatization.
- Named Entity Recognition: Identifying and classifying named entities in text.
- Text Representation: Exploring various methods to represent text data.
- Word Embedding: Implementing word embedding techniques.
- Chatbots:
- 7.1 Overview: Introduction to building chatbots.
Additional files:
req.txt: Contains the list of required Python packages.r1.txt: Sample text data used in tutorials..gitignore: Specifies files and directories to be ignored by Git.
Installation
- Clone the repository:
bash
git clone https://github.com/nabeelalikhan0/NLP-Tutorial.git
cd NLP-Tutorial
- Create and activate a virtual environment:
bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
- Install the required packages:
bash
pip install -r req.txt
Usage
Each chapter is contained within its respective folder. Navigate to the desired chapter and run the Python scripts or Jupyter notebooks provided to explore the concepts and implementations.
For example, to explore Chapter 2:
bash
cd 2. Tokenization With SpaCy
python tokenization_spacy.py
Chapter Overview
- Chapter 1: Learn how to use regular expressions for text processing tasks.
- Chapter 2: Understand tokenization and how to implement it using SpaCy.
- Chapter 3: Explore stemming and lemmatization techniques to normalize text.
- Chapter 4: Dive into named entity recognition to extract meaningful information.
- Chapter 5: Study different methods to represent text data numerically.
- Chapter 6: Implement word embedding techniques to capture semantic meaning.
- Chapter 7: Get an overview of building chatbots and conversational agents.
Chapter 6: Word Embedding
Important: Before running the scripts in Chapter 6, ensure you have downloaded the necessary pre-trained word embedding models. These models are essential for the tutorials and may not be included in the repository due to their size.
Please download the required models from the official sources or as specified in the chapter's instructions.
Contributing
Contributions are welcome! If you have suggestions for improvements or additional tutorials, feel free to fork the repository and submit a pull request.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Contact
Created by Nabeel Ali Khan.
Feel free to reach out for questions, feedback, or collaboration opportunities.