Daniel S. Paiva

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We present the rags (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal(More)
The RAGS project aims to develop a reference architecture for natural language generation, to facilitate modular development of NLG systams as well as evaluation of components, systems and algorithms. This paper gives an overview of the proposed framework, describing an abstract data model with five levels of representation: Conceptual, Semantic,(More)
The RAGS proposals for generic specification of NLG systems includes a detailed account of data representation, but only an outline view of processing aspects. In this paper we introduce a modular processing architecture with a concrete implementation which aims to meet the RAGS goals of transparency and reusability. We illustrate the model with the RICHES(More)
We present the rags (Reference Architecture for Generation Systems) framework, a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal(More)
This paper introduces an approach to representing the kinds of information that components in a natural language generation (NLG) system will need to communicate to one another. This information may be partial, may involve more than one level of analysis and may need to include information about the history of a derivation. We present a general(More)
The RAGS project aims to define a reference architecture for Natural Language Generation (NLG) systems. Currently the major part of this architecture consists of a set of datatype definitions for specifying the input and output formats for modules within NLG systems. In this paper we describe our efforts to reinterpret an existing NLG system in terms of(More)
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