One of the first steps in any NLP process is tokenization, which involves breaking down text into individual units, such as words or sentences. This allows the system to analyze each piece separately. After tokenization, the next step is often Part-of-Speech (POS) tagging, where each word is labeled with its grammatical role—like noun, verb, or adjective. For example, in the sentence “NLP is powerful,” the system would tag “NLP” as a noun and “powerful” as an adjective. These basic techniques are essential for understanding sentence structure and context.
Named Entity Recognition (NER) is a more advanced NLP technique that identifies and classifies key information in text, such as names of people, organizations, locations, or dates. For example, in the sentence “Apple announced its new product in California,” NER would label “Apple” as an organization and “California” as a location. Another widely used method is sentiment analysis, which determines the emotional tone behind a piece of text—whether it's positive, negative, or neutral. This is commonly used in social media monitoring, customer feedback analysis, and market research.
NLP also enables computers to perform complex language-based tasks like machine translation, which converts text from one language to another. Services like Google Translate use NLP models trained on massive multilingual datasets to make this possible. Another powerful technique is text summarization, which generates a shortened version of a longer text while preserving its meaning. This is useful for quickly extracting key insights from articles, reports, or research papers. These techniques showcase the evolving power of NLP to process and generate human-like language across various applications
