Natural Language Processing (NLP) is a field of Artificial Intelligence that gives computers the ability to read, understand, interpret, and generate human language. It works by breaking down text or speech into smaller pieces, analyzing the grammatical structure and meaning, and then using algorithms to perform a specific task like translation or sentiment analysis.
Analogy: Teaching a Computer to Read 📖
Teaching a computer language is like teaching a child to read. There are several stages:
- First, they learn to recognize individual letters and words. This is Tokenization.
- Next, they learn the rules of grammar—what’s a noun, what’s a verb, and how they fit together in a sentence. This is Parsing and Tagging.
- Finally, they learn to understand the actual meaning, context, and sentiment of the story. This is Semantic Analysis.
The Basic Steps of NLP
While modern methods are more complex, they are all built on these foundational steps.
1. Text Preprocessing (Breaking Down the Language)
This is the cleaning phase to make the text uniform and easier for a machine to understand.
- Tokenization: Splitting a sentence into individual words or sub-words called “tokens.” For example, “The cat sat” becomes
["The", "cat", "sat"]. - Stop Word Removal: Removing common words (like “the,” “is,” “a”) that add little meaning.
- Lemmatization/Stemming: Reducing words to their root form (e.g., “running,” “ran,” and “runs” all become “run”).
2. Understanding Structure and Meaning
Once the text is clean, the model tries to understand it.
- Part-of-Speech (POS) Tagging: Identifying each word as a noun, verb, adjective, etc.
- Named Entity Recognition (NER): Finding and classifying key entities in the text, such as names of people, organizations, and locations.
3. The Modern Approach: Transformer Models
Today, NLP is dominated by massive models like GPT-4, built on the Transformer architecture. These Large Language Models (LLMs) learn the patterns, grammar, and context of language by processing enormous amounts of text from the internet. They can perform complex tasks without needing to be explicitly programmed for each one because they have developed a deep, statistical understanding of how human language works.
Real-World Examples of NLP
- Spam Filters: Classifying your emails.
- Virtual Assistants: Siri and Alexa understanding your commands.
- Language Translation: Google Translate converting Spanish to English.
- Sentiment Analysis: A company analyzing tweets to see if people are happy or angry about their new product.
Step 2: Offer Next Step
The explanation of Natural Language Processing is now complete. The next topic on our list is about vector databases and why LLMs need them. Shall I prepare that for you?