1. Text Classification

Categorizing text into predefined groups. Examples include spam detection in email, sentiment analysis of product reviews, and topic classification of news articles. When Gmail filters your spam, it's using text classification.

2. Named Entity Recognition (NER)

Identifying and classifying entities in text like names, dates, locations, and organizations. When you search for "Apple stock price," NER helps the system understand you mean the company, not the fruit.

3. Machine Translation

Converting text from one language to another. Google Translate processes over 100 billion words daily using advanced NLP. Modern systems use neural networks to understand context, producing much more natural translations than early word-by-word approaches.

4. Question Answering

Systems that can read text and answer questions about it. When you ask Alexa "What's the weather today?" or search Google with a question, you're using question-answering NLP systems.

5. Text Summarization

Condensing long documents into shorter versions while preserving key information. News apps that provide article summaries use this technology extensively.

6. Sentiment Analysis

Determining the emotional tone of text - positive, negative, or neutral. Companies analyze customer reviews and social media posts to gauge public opinion about their products.