Design issues in building natural language generators

1997

In this article, we g i v e a n o verview of Natural Language Generation (nlg) from an applied system-building perspective. The article includes a discussion of when nlg techniques should be used suggestions for carrying out requirements analyses and a description of the basic nlg tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Throughout, the emphasis is on established techniques that can be used to build simple but practical working systems now. We also provide pointers to techniques in the literature that are appropriate for more complicated scenarios.

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:

Proceedings of the IMPACTS …, 2000

We describe how Natural Language Generation (nlg) Technology is used in the Mile system. Mile is a web-based system for accessing maritime rules and regulations. We explain how the architecture of the system was derived from a set of user requirements and focus on the role of nlg in this architecture. More speci cally, we describe how multilingual generation of answers to queries and the use of the nlg-based wysiwym-technology for multilingual query formulation t into the architecture. The architecture is di erent from conventional dialogue systems in that it is centered around a dialogue database which allows the user to store, retrieve and manipulate so-called dialogue histories (i.e., language-independent records of the dialogue between the user and the system).

Natural Language Generation is the task of gener- ating natural language text suitable for human consumption from machine representation of facts which can be pre-structured in some linguistically amenable fashion, or completely unstructured. An ontology is a formal explicit description of concepts in a domain of discourse. An ontology is considered as a formal knowledge repository which can be used as a resource for NLG tasks. A domain ontology will provides the input for content determination and micro-planing of NLG task. A linguistic ontology can be used for lexical realization. The logically structured manner of knowledge organization within an ontology enables to perform reasoning tasks like Con- sistency checking, Concept Satisfiability, Concept Subsumption and Instance Checking. These types of logical inferencing actions will be applied to derive descriptive texts as answers to user queries from ontologies. Thus a simple natural language based Question Answering system can be implement, guided by robust NLG techniques that act upon ontologies. Some of the tools for constructing ontologies ( Protege, Natural OWL, etc.) and their combination with NLG process will also be discussed.

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.

Oxford Handbooks Online, 2005

Communication via a natural language requires two fundamental skills, producing 'text' (written or spoken) and understanding it. Th is chapter introduces newcomers to the fi eld of computational approaches to the former-natural language generation (henceforth NLG)-showing some of the theoretical and practical problems that linguists, computer scientists, and psychologists have encountered when trying to explain how language works in machines or in our minds. ¹ Authors in alphabetical order     . G I: W  N L G (C, L,  S D)

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.

The Kluwer International Series in Engineering and Computer Science, 1991

A number of collections of papers from the field of natural language generation (NLG) have been published over the last few years: Kempen (1987), Zock and Sabah (1988), Dale, Mellish, and Zock (1990), and now the present volume. All have in common that they are derived in one way or another from workshops on the subject, and should therefore make available new and often exploratory research in a timely fashion. If such a book is to be more than a conference proceedings, it has to do a little more too, of course; it should present the research in more detail than a conference proceedings would, there should be greater cohesion amongst the papers, and it should be produced to an appropriate standard. The present book, like its predecessors, succeeds on some counts but fails on others. The papers in the book are organized into three strands, described in turn below: text planning, lexical choice, and grammatical resources. The balance between these is rather skewed, however: the first section contains eight papers, and the second and third contain only three papers each.

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.

Natural Language Engineering, 2004

Generic software architectures aim to support re-use of components, focusing of research and development effort, and evaluation and comparison of approaches. In the field of natural language processing, generic frameworks for understanding have been successfully deployed to meet all of these aims, but nothing comparable yet exists for generation. The nature of the task itself, and the current methodologies available to research it, seem to make it more difficult to reach the necessary level of consensus to support generic proposals. Recent work has made progress towards establishing a generic framework for generation at the functional level, but left open the issue of actual implementation. In this paper, we discuss the requirements for such an implementation layer for generation systems, drawing on two initial attempts to implement it. We argue that it is possible and useful to distinguish "functional architecture" from "implementation architecture" for generation systems. 1 The Case for a Generic Software Architecture for NLG Most natural language generation (NLG) systems have some kind of modular structure. The individual modules may differ in complex ways, according to whether they are based on symbolic or statistical models, what particular linguistic theories they embrace and so on. Ideally, such modules could be reused in other NLG systems. This would avoid duplication of work, allow realistic research specialisation and allow empirical comparison of different approaches. Examples of ideas that might give rise to reusable modules include: