What is Natural Language Processing?
AI Summary
Natural language processing (NLP) is the field that enables computers to understand, interpret, and generate human language using techniques such as machine learning and deep learning for applications ranging from chatbots to text analysis.
Why It’s Important
- Makes tech accessible by allowing interaction through everyday language.
- Automates and improves human-like tasks: chatbots, virtual assistants, summarization, translation, and more
- Extracts insights from unstructured data—sentiment, intent, key information—strikingly valuable across sectors: enterprise automation, search relevance, voice systems, customer support, and specialty domains like healthcare and legal
How NLP Works
- Preprocessing: Tokenizing, lowercasing, stemming, lemmatization, and cleaning raw text or speech input
- Modeling approaches:
- Rule-based: Hand-crafted grammars and keywords and limited scalability
- Statistical: Uses probabilistic models trained on labeled corpora (e.g. POS tagging, language modeling)
- Deep learning: Leverages neural architectures such as RNNs, sequence-to-sequence models, and transformers (for example, self-attention, BERT, GPT) trained via self-supervised learning to handle complex language tasks
- NLP pipeline: Involves preprocessing → feature extraction or/embedding → modeling (e.g., classification or generation) → postprocessing.
Key Components and Features
- Text and speech understanding: Systems that parse, interpret, and extract meaning from written and spoken language.
- Language generation: Synthesis of coherent, context-aware text or speech (e.g., summarization, response generation).
- Computational linguistics + ML: Combines grammar rules, syntactic parsing, statistical models, and neural networks.
- Tasks: Includes tokenization, part-of-speech tagging, named-entity recognition, sentiment analysis, machine translation, and coreference resolution.
Relevant Resources
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Related Topics
- Artificial Intelligence (AI): The broader discipline of building systems that can perform tasks typically requiring human intelligence, such as reasoning, perception, and decision-making.
- AI Technology: The set of computational methods, systems, and hardware used to create, deploy, and scale artificial intelligence applications.
- Deep Learning: A subset of machine learning that uses multi-layer neural networks to learn patterns and features directly from raw data.
- Machine Learning (ML): Techniques powering training of NLP models using data-driven learning.