NLP

Natural Language Processing is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language.

It means analyzing grammar, semantics, and pragmatics to derive meaning from text or speech.

Unstructured language data such as written text and spoken language.

Language generation is the ability of a system to produce coherent and meaningful human language output.

It enables machines to communicate with humans naturally and to process vast amounts of language data efficiently.

Search engines, virtual assistants, chatbots, sentiment analysis, and speech recognition systems.

NLP enables sentiment analysis, opinion mining, and chatbots that improve customer support and decision-making.

NLP research began in the 1950s alongside early artificial intelligence research.

Early NLP systems relied heavily on handcrafted rules and explicit linguistic knowledge.

Systems that process language using manually created grammatical, syntactic, or semantic rules.

Their behavior is deterministic and easier to interpret and debug.

They struggle with ambiguity, language variation, and require constant manual maintenance.

The introduction of statistical models that learn patterns from large text corpora.

Data-driven approaches that use probabilities to model and predict language patterns.

N-grams, Hidden Markov Models, and Bayesian methods.

They learn from data and can generalize across domains and languages.

Machine learning allows systems to automatically learn language patterns and representations from data.

Deep learning automatically learns complex features and long-range dependencies without manual feature engineering.

Recurrent Neural Networks, LSTMs, and Transformer-based models.

They capture contextual relationships across entire texts and enable state-of-the-art performance.

Very large neural models trained on massive text datasets to perform a wide range of NLP tasks.

Training involves processing massive datasets and optimizing millions or billions of parameters.

They are less interpretable and harder to explain than rule-based systems.

Techniques that allow systems to convert spoken or written language into machine-readable representations.

A system that uses NLP to interact with users via text or speech to perform tasks or answer questions.

The task of determining whether language expresses positive, negative, or neutral sentiment.

The extraction and analysis of opinions and attitudes expressed in text.

To analyze customer feedback, social media posts, and public opinion.

The automatic translation of text from one language to another.

Creating responses in chatbots, virtual assistants, and automated content creation systems.

Models trained to follow human instructions more accurately and align with user intent.

They enable understanding and translation across multiple languages within a single system.

NLP that integrates text with other modalities such as images, audio, or video.

The ability of a model to perform tasks using only a small number of examples.

The ability of a model to perform tasks without seeing task-specific examples.

NLP systems can reflect societal biases and must be designed to ensure fairness and transparency.

To enable computers to understand and generate human language accurately, ethically, and contextually.