Natural Language Processing Tutorial

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and humans through natural language. This tutorial will guide you through the basics of NLP, including text processing, sentiment analysis, and more.

Getting Started

To begin with NLP, you need to have a basic understanding of Python and some popular libraries such as NLTK and spaCy. You can find more information about setting up your environment in our Python Setup Guide.

Key Concepts

• Tokenization: The process of splitting text into words or tokens.
• Part-of-Speech Tagging: Assigning a grammatical category to each word in a text.
• Named Entity Recognition (NER): Identifying and categorizing named entities in text.
• Sentiment Analysis: Determining the sentiment of a text, whether it's positive, negative, or neutral.

Examples

Here are some examples of how NLP can be used:

• Text Classification: Categorizing text into predefined categories, such as spam or not spam.
• Machine Translation: Translating text from one language to another.
• Chatbots: Building chatbots that can understand and respond to natural language input.

Resources

• NLTK Documentation
• spaCy Documentation

NLP Example

Conclusion

Natural Language Processing is a powerful tool for understanding and interacting with human language. By following this tutorial, you'll be well on your way to building your own NLP applications.