What is NLP?
Natural Language Processing bridges the gap between human communication and computer understanding. It allows machines to work with unstructured text and speech - extracting meaning, identifying patterns, and enabling contextual responses.
NLP tasks fall broadly into two categories:
- Natural Language Understanding (NLU): Interpreting input (e.g., intent detection, sentiment analysis).
- Natural Language Generation (NLG): Producing output (e.g., writing summaries or replies).
Modern NLP relies on Large Language Models (LLMs) trained on billions of words to perform complex tasks like summarization, question answering, and conversational dialogue. It combines linguistics, computer science, and machine learning to help systems process text or speech in ways that mimic human comprehension. NLP powers everyday applications such as chatbots, translation services, search engines, and voice assistants.
How NLP Works
- Text Input: Raw text or speech is captured and converted into digital form.
- Tokenization: Breaks sentences into words or sub-words for analysis.
- Part-of-Speech Tagging & Parsing: Identifies grammatical structure and relationships.
- Feature Extraction: Converts text into numerical vectors using embeddings.
- Model Processing: ML or deep-learning models analyze meaning and context.
- Output Generation: Returns results - sentiment labels, translations, or generated responses.
