Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. This tutorial will provide an overview of the basics of NLP, including key concepts, techniques, and tools.
Key Concepts
- Tokenization: The process of splitting text into words, phrases, symbols, or other meaningful elements called tokens.
- Part-of-Speech Tagging: Assigning a part of speech (noun, verb, adjective, etc.) to each word in a sentence.
- Named Entity Recognition (NER): Identifying and categorizing named entities in text, such as names, organizations, and locations.
- Sentiment Analysis: Determining the sentiment of a piece of text, such as whether it is positive, negative, or neutral.
Techniques
- Machine Learning: Using algorithms to analyze and make predictions based on data.
- Deep Learning: A subset of machine learning that uses neural networks to model complex patterns in data.
- Transfer Learning: Using a pre-trained model on a new task, reducing the need for large amounts of labeled data.
Tools
- NLTK: A leading platform for building Python programs to work with human language data.
- spaCy: An industrial-strength natural language processing library that provides easy-to-use APIs for various NLP tasks.
- TensorFlow: An open-source machine learning framework developed by Google Brain.
Learn More
For more in-depth information on NLP, check out our Introduction to NLP.
NLP in Action