Natural language generation (NLG)

 Natural language generation (NLG) is a subfield of artificial intelligence that focuses on the generation of natural language text or speech from structured data. It involves the automatic production of human-like text or speech based on predefined rules, templates, or statistical models.

NLG systems take structured data, such as numerical data, facts, or sensory inputs, and transform it into coherent, meaningful, and linguistically appropriate sentences or narratives. The process typically involves several steps, including data selection, data preprocessing, content determination, text planning, sentence realization, and surface realization.

NLG finds applications in various domains, including automated journalism, chatbots, customer service, e-commerce product descriptions, business intelligence reporting, and personalized recommendations. It can create reports, summaries, explanations, stories, and other forms of text that are tailored to specific audiences and purposes.

In order to generate natural and fluent language output, NLG systems employ various linguistic techniques, including grammar parsing, sentence generation, vocabulary selection, and language style adaptation. They can also incorporate machine learning and deep learning approaches to learn from large corpora of text in order to improve the quality of generated language.

Overall, NLG plays a crucial role in enabling machines to communicate effectively with humans, providing valuable information and insights in a format that humans can easily understand and interact with.