4.
Explanation
Data Input and Structuring: NLG systems start with structured data, which can come from various sources such as databases, spreadsheets, or other structured formats. This data is organized and prepared for language generation.
Content Planning: The system decides what content to generate and how to structure it. This includes selecting the main message, determining the tone, and considering the context of the communication.
Linguistic Analysis: NLG systems analyse the structured data and apply linguistic rules to convert it into a coherent narrative. This involves understanding the relationships between data points and their linguistic representation.
Text Generation Techniques: Various techniques can be used for text generation, including rule-based methods, statistical models, and more advanced approaches like neural networks and deep learning. These techniques determine how words and phrases are formed.
Stylistic and Grammar Rules: The generated text is checked for adherence to grammatical rules, syntax, and style guidelines to ensure that it is fluent and comprehensible.
Personalization and Adaptation: Some NLG systems are designed to personalize content by considering user preferences, historical data, or specific requirements. This allows for tailored and relevant language generation.
Quality Assurance: Quality control steps are implemented to verify the accuracy and coherence of the generated content. This may involve automated checks or human review.
Output Medium: The final generated language can be presented in various forms, such as written text, spoken language through text-to-speech conversion, or other mediums depending on the application's needs.