Skosmos: omtd_OperationScheme: Natural Language Generation

> Natural Language Generation

Natural Language Generation

Natural Language Generation (NLG) is concerned with turning some usually non-linguistic representation of information and intended effect into fluent text preserving both meaning and intention. NLG systems often identify the content to be verbalized. They structure the document into interrelated sentence-sized chunks, choose appropriate words, aggregate and elide information to ensure fluency, create contextually appropriate referring expressions, such as pronouns, and follow grammatical constraints of the chosen language. All this is achieved using knowledge about the world and the domain of dicsourse, about communication and about languages. NLG components are used for e.g. automatic report generation, document authoring, dialogue, concept-to-speech, multi-modal and machine translation systems. Evaluating the correctness and the appropriateness of generated text is a research theme on its own since there is usually no single correct solution. One important way to tackle the problem consists in creating reference corpora and performing shared evaluation tasks, e.g. on generating referring expressions. However, this is not intended to replace less formal evaluation strategies such as human assessments.

The task/process of generating natural language text from some non-linguistic representation of information

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