Discourse strategies for generating natural-language text
Artificial Intelligence
Pragmatics and natural language generation
Artificial Intelligence
Expressibility and the Problem of Efficient Text Planning
Expressibility and the Problem of Efficient Text Planning
The Linguistic Basis of Text Generation
The Linguistic Basis of Text Generation
Planning English Sentences
Increasing Cohesion in Automatically Generated Natural Language Texts
AI*IA '93 Proceedings of the Third Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Generating referring expressions in a domain of objects and processes (language representation)
Generating referring expressions in a domain of objects and processes (language representation)
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A natural language generation system is typically constituted by two main components: a content planning component (e.g., text planner or dialogue act planner) and a linguistic realization component. But, this is not sufficient since, on the one hand, the message built by the content planning component is generally not adequately detailed in order to control the many possibilities for its expression and, on the other hand, the content planner cannot influence the way in which the message will be verbalized. Generation systems require a third component, called the micro-planning (or sentence planning or phrasing) component, which acts as an intermediary between the pragmatico-semantic level and the purely syntactic level. The micro-planner is responsible for transforming the message into a textual structure. For this transformation to be achieved, grammatical and lexical resources must be selected.