Text generation: using discourse strategies and focus constraints to generate natural language text
Text generation: using discourse strategies and focus constraints to generate natural language text
Attention, intentions, and the structure of discourse
Computational Linguistics
The blackboard model of problem solving
AI Magazine
Principles of artificial intelligence
Principles of artificial intelligence
The “GENERATION GAP”: the problem of expressibility in text planning
The “GENERATION GAP”: the problem of expressibility in text planning
Locally organized text generation
Locally organized text generation
Negotiation, feedback, and perspective within natural language generation
Negotiation, feedback, and perspective within natural language generation
Generating Natural Language under Pragmatic Constraints
Generating Natural Language under Pragmatic Constraints
The Linguistic Basis of Text Generation
The Linguistic Basis of Text Generation
Planning English Sentences
Controlling Content Realization with Functional Unification Grammars
Proceedings of the 6th International Workshop on Natural Language Generation: Aspects of Automated Natural Language Generation
Integrated Natural Language Generation Systems
Proceedings of the 6th International Workshop on Natural Language Generation: Aspects of Automated Natural Language Generation
Generating referring expressions in a domain of objects and processes (language representation)
Generating referring expressions in a domain of objects and processes (language representation)
Telegram: a grammar formalism for language planning
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
An overview of the Nigel text generation grammar
ACL '83 Proceedings of the 21st annual meeting on Association for Computational Linguistics
Planning text for advisory dialogues
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Planning coherent multisentential text
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Two types of planning in language generation
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
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Natural language generation is usually divided into separate text planning and linguistic components. This division, though, assumes that the two components can operate independently, which is not always true. The IGEN generator eliminates the need for this assumption; it handles interactions between the components without sacrificing the advantages of modularity. IGEN accomplishes this by means of annotations that its linguistic component places on the structures it builds; these annotations provide an abstract description of the effects of particular linguistic choices, allowing the planner to evaluate these choices without needing any linguistic knowledge. This approach allows IGEN to vary the work done by each component independently, even in cases where the final output depends on interactions between them. In addition, since IGEN explicitly models the effects of linguistic choices, it can gracefully handle situations where the available time or linguistic resources are limited.