Systemic Functional Grammar Natural Language Generation

This volume deals with the computational application of systemic functional grammar (SFG) for natural language generation. In particular, it describes the implementation of a fragment of the grammar of German in the computational framework of KOMET-PENMAN for multilingual generation. The text also presents a specification of explicit well-formedness constraints on syntagmatic structure which are defined in the form of typed feature structures. It thus achieves a model of systemic functional grammar that unites both the strengths of systemics, such as stratification, functional diversification and the orientation to context, and the kind of syntactic generalizations that are typically found in modern, syntagmatically-focused computational grammars.

This book explains how to build Natural Language Generation (NLG) systems - computer software systems which use techniques from artificial intelligence and computational linguistics to automatically generate understandable texts in English or other human languages, either in isolation or as part of multimedia documents, Web pages, and speech output systems. Typically starting from some non-linguistic representation of information as input, NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It also discusses engineering issues such as system architecture, requirements analysis, and the integration of text generation into multimedia and speech output systems.

This book aims to inform researchers with an interest in natural language generation about advances in the field. It is organised around four topics – system architectures, content planning, discourse planning and realisation in linguistic form - and it presents some of the most important works in this area of research.

Text generation is the processing of information that is stored at a higher level than grammatical structures and lexical items (such as sentences and words), organizing and re-expressing it so that it can appear as a worded text. Of course it interests those working on artificial intelligence, but it should also interest linguists as a linguistic research task.

This collection of essays deals with the problem of natural language generation, that is: how to simulate by computer the determinism, organization and expression of thoughts in oral or written form. Compared to sentence or text-analysis (parsing) little work has been done in the field of generation, which is still a young discipline.

This volume deals with the computational application of systemic functional grammar (SFG) for natural language generation. In particular, it describes the implementation of a fragment of the grammar of German in the computational framework of KOMET-PENMAN for multilingual generation. The text also presents a specification of explicit well-formedness constraints on syntagmatic structure which are defined in the form of typed feature structures. It thus achieves a model of systemic functional grammar that unites both the strengths of systemics, such as stratification, functional diversification and the orientation to context, and the kind of syntactic generalizations that are typically found in modern, syntagmatically-focused computational grammars.

One of the aims of Natural Language Processing is to facilitate .the use of computers by allowing their users to communicate in natural language. There are two important aspects to person-machine communication: understanding and generating. While natural language understanding has been a major focus of research, natural language generation is a relatively new and increasingly active field of research. This book presents an overview of the state of the art in natural language generation, describing both new results and directions for new research. The principal emphasis of natural language generation is not only to facili tate the use of computers but also to develop a computational theory of human language ability. In doing so, it is a tool for extending, clarifying and verifying theories that have been put forth in linguistics, psychology and sociology about how people communicate. A natural language generator will typically have access to a large body of knowledge from which to select information to present to users as well as numer of expressing it. Generating a text can thus be seen as a problem of ous ways decision-making under multiple constraints: constraints from the propositional knowledge at hand, from the linguistic tools available, from the communicative goals and intentions to be achieved, from the audience the text is aimed at and from the situation and past discourse. Researchers in generation try to identify the factors involved in this process and determine how best to represent the factors and their dependencies.

Proceedings of the NATO Advanced Research Workshop, Nijmegen, The Netherlands, August 19-23, 1986