Implementation architectures for natural language generation (original) (raw)
Related papers
2004
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 from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations. * This is a revised and updated version of the paper "A Reference Architecture for Generation Systems" which appeared (in error) in Natural Language Engineering 10(3/4) the Special Issue on Software Architectures for Language Engineering. This version should be cited in preference to the earlier one. 1 This survey drew on Reiter's original (Reiter 1994) formulation of the model. The later (Reiter and Dale 2000) formulation uses slightly different terminology, which we also use here, but for our purposes is otherwise not significantly different. 2 The systems surveyed were:
A reference architecture for natural language generation systems
Natural Language …, 2006
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 from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.
Towards a Reference Architecture for Natural Language Generation Systems
1999
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 from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.
Towards a Reference Architecture for Natural Language Generation Systems, The RAGS project
1999
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 from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.
Reinterpretation of an existing NLG system in a Generic Generation Architecture
Proceedings of the …, 2000
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 these definitions. The system chosen was the Caption Generation System.
Architectures for Natural Language Generation: Problems and Perspectives
1993
Current research in natural language generation is situated in a computational linguistics tradition that was founded several decades ago. We critically analyse some of the architectural assumptions underlying existing systems and point out some problems in the domains of text planning and lexicalization. Guided by the identification of major generation challenges viewed from the angles of knowledge-based systems and cognitive psychology, we sketch some new directions for future research.
Building natural language generation systems
Natural Language Engineering, 1997
The book is about natural language generation (NLG), which is a sub®eld of arti®cial intelligence and computational linguistics that is concerned with building computer software systems that can produce meaningful texts in English or other human languages from some underlying non-linguistic representation of information. In the introduction, the ®eld of NLG is brie¯y characterized from research-and applicationoriented perspectives and illustrated by screen shots produced by several systems. Then, conditions for bene®cial uses of this technology are elaborated and contrasted with conditions where other techniques are more appropriate. Moreover, methods for determining the intended functionality of a system to be built are discussed. The main sections of the book are devoted to the prototypical architecture of application-oriented NLG systems and their major processing phases: document planning, microplanning and surface realization. Each of these three phases is illustrated by a number of detailed examples, demonstrating the successive re®nements of utterance speci®cations in the course of processing. In the ®nal section, embedding of the natural language processing technology is discussed featuring typography, combined uses with graphics and hypertext, as well as integration with speech. The methods are illustrated by a large number of examples Ð the book contains more than 120 ®gures on its 248 pages. At the end of each section, a number of useful references for further reading are related to the section topics. In the appendix, a table summarizing the 35 systems referred in the book is given.
2014
At the start of the project, research in the field of dialogue systems had not addressed issues of natural language generation that are an integral part of the communication cycle. Natural language generation (NLG) research had achieved practical solutions to specific tasks as independent research modules, but they were difficult to interrelate and integrate with other applications. The first goal of the project was the development of a flexible and modular software solution, capable of working with ontologies and the emotional content of messages. This solution should provide a set of reusable software components capable of generating texts suitable for different tasks in different domains. The second goal of the project was to study the application of NLG in spoken dialogue systems in a domotic environment.
2009
At the start of the project, research in the field of dialogue systems had not addressed issues of natural language generation that are an integral part of the communication cycle. Natural language generation (NLG) research had achieved practical solutions to specific tasks as independent research modules, but they were difficult tointerrelate and integrate with other applications. The first goal of the project was the development of a flexible and modular software solution, capable of working with ontologies and the emotional content of messages. This solution should provide a set of reusable software components capable of generating texts suitable for different tasks in different domains. The second goal of the project was to study the application of NLG in spoken dialogue systems in a domotic environment.