Welcome to the NLP Notebooks repository! This repository contains a collection of Jupyter notebooks that demonstrate how to use natural language processing techniques to analyze text data.

• Introduction
• Notebooks
• Installation
• Usage
• Contributing
• License
• Other Notebooks

Natural language processing (NLP) is a field of computer science that focuses on the interaction between computers and humans using natural language. NLP techniques are used to analyze and understand human language, and can be applied to a wide range of tasks, including sentiment analysis, topic modeling, named entity recognition, word embedding, tokenization, and lemmatization.

This repository contains a collection of Jupyter notebooks that demonstrate how to use NLP techniques to analyze text data. Whether you're new to NLP or an experienced practitioner, these notebooks will help you get up and running with the latest techniques and tools in the field.

The notebooks in this repository cover a range of topics, including:

• Sentiment analysis
• Topic modeling
• Named entity recognition
• Word embedding
• Tokenization
• Lemmatization

To install the required packages for this repository, run the following command:

pip install -r requirements.txt

To use the notebooks in this repository, simply open them in Jupyter Notebook or JupyterLab. Each notebook contains detailed instructions on how to use the NLP techniques covered in the notebook.

Contributions are welcome! If you would like to contribute to this repository, please open an issue or submit a pull request.

This repository is licensed under the MIT License. See LICENSE for more information.

Here are some other NLP resources you might find helpful:

• Natural Language Processing with Python: This book provides a practical introduction to NLP using the Python programming language and the Natural Language Toolkit (NLTK).
• Speech and Language Processing: This book provides a comprehensive introduction to NLP and speech processing, covering both statistical and symbolic approaches.
• spaCy: spaCy is an open-source library for advanced NLP in Python. It's designed specifically for production use and helps you build applications that process and "understand" large volumes of text.
• Gensim: Gensim is an open-source library for unsupervised topic modeling and natural language processing, using modern statistical machine learning.
• Stanford CoreNLP: Stanford CoreNLP is a suite of production-ready natural language processing tools written in Java. It provides a set of human language technology tools that can be used to analyze text data.
• AllenNLP: AllenNLP is an open-source NLP research library built on PyTorch. It provides a suite of pre-built models for common NLP tasks, as well as a framework for building custom models.

Here are some other NLP notebook repositories:

• nlp-with-transformers/notebooks: This repository contains example code from the O’Reilly book “Natural Language Processing with Transformers”.
• nlptown/nlp-notebooks: This repository contains a collection of notebooks for natural language processing.
• nlp-with-transformers: This repository contains notebooks and materials for the O’Reilly book “Natural Language Processing with Transformers”.

I hope this helps! Let me know if you have any other questions. 😊 I hope this helps!😊