Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. It involves the ability of machines to understand, interpret, and generate human language. This tutorial will provide an introduction to NLP, covering basic concepts, techniques, and applications.
Basic Concepts
- Tokenization: The process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens.
- Part-of-Speech Tagging: Assigning a part of speech to each word in a sentence, such as noun, verb, adjective, etc.
- Named Entity Recognition (NER): Identifying and classifying named entities in text, such as names of people, organizations, locations, etc.
- Sentiment Analysis: Determining the sentiment or opinion expressed in a piece of text, such as positive, negative, or neutral.
Techniques
- Machine Learning: Using algorithms to learn from data and make predictions or decisions.
- 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, which can significantly reduce the amount of training data required.
Applications
- Chatbots: Automated systems that can interact with users in natural language.
- Voice Assistants: Systems that can understand and respond to spoken language, such as Siri and Alexa.
- Machine Translation: Automatically translating text from one language to another.
- Text Summarization: Generating a concise summary of a longer text.
Resources
For further reading, you can explore the following resources:
NLP Example