We examine the problem of generating definite noun phrases that are appropri-ate referring expressions: that is, noun phrases that (a) successfully identify the intended referent to the hearer whilst (b) not conveying to him or her any false conversational implicatures (Grice, 1975). We review several possible computa-tional interpretotions of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended algorithm in detail, along with a specification of the resources a host system must provide in order to make use of the algorithm, and an implementation used in the natural language generation component of the IDAS system. 1.

I survey some recent applications-oriented NL generation systems, and claim that despite very different theoretical backgrounds, these systems have a remarkably similar architecture in terms of the modules they divide the generation process into, the computations these modules perform, and the way the modules interact with each other. I also compare this 'consensus architecture' among applied NLG systems with psycholinguistic knowledge about how humans speak, and argue that at least some aspects of the consensns architecture seem to be in agreement with what is known about human language production, despite the fact that psycholinguistic plausibility was not in general a goal of the developers of the surveyed systems.

We simplify previous work in the development of algorithms for the generation of referring expre sions while at the same time taking account of psychohnguistic findings and transcript data. The result is a straightforward algorithm that is computationally tractable, sensitive to the preferences of human users, and reasonably domain-independent. We pr( vide a specification of the resources a host system must provide in order to make use of the algorithm, and describe an implementation used in the IO^S sys- tem.

Multilingual instructions generation has been the object of many studies recently, motivated by the increased need to produce multilingual manuals coupled with the cost of technical writing and translating. These studies concentrate on the automatic generation of instructions, leaving technical writers out of the loop. In many cases, however, it is not possible to dispense with human intervention entirely, for at least two reasons. First, the system must be provided with a semantic knowledge base from which the instructions can be generated. Second, it is the technical writers who have the expertise necessary for producing instructions appropriate for a specific product or company, and it is not necessarily an easy task to make this expertise available to a system. The results of a requirement analysis study confirm the view that the most useful tool is not a stand-alone writing tool but rather one that supports technical writers in their task. In this paper, we describe such a support tool, which we developed based on the results of our user requirement analysis.

Natural-language generation (NLG) techniques can be used to automatically produce technical documentation from a domain knowledge base and linguistic and contextual models. We discuss this application of NLG technology from both a technical and a usefulness (costs and benefits) perspective. This discussion is based largely on our experiences with the idas documentation-generation project, and the reactions various interested people from industry have had to idas. We hope that this summary of our experiences with idas and the lessons we have learned from it will be beneficial for other researchers who wish to build technical-documentation generation systems.

The Intelligent Documentation Advisory System generates on-line documentation and help messages from a domain knowledge base, using natural-language (NL) generation techniques.

This thesis develops a new approach to automatic language generation that focuses on the need to produce a range of different paraphrases from the same input representation. One novelty of the system is its solidly grounding representations of word meaning in a background knowledge base, which enables the production of paraphrases stemming from certain inferences, rather than from purely lexical relationships alone. The system is designed in such a way that the paraphrasing mechanism extends naturally to a multilingual generator; specifically, we will be concerned with producing English and German sentences. The focus of the system is on lexical paraphrases, and one of the contributions of the thesis is in identifying, analyzing and extending relevant linguistic research so that it can be used to handle...

The IDAS natural-language generation system uses a KL-ONE type classifier to perform content determination, surface realisation, and part of text planning. Generation-by-classification allows IDAS to use a single representation and reasoning component for both domain and linguistic knowledge, which is difficult for systems based on unification or systemic generation techniques.

This paper addresses this issue in the context of the expression of procedural relations between actions in instructional text. It employs the following four step approach to achieve this goal: (1) Collect a corpus of the relevant text type; (2) Perform a detailed linguistic study of a portion of this corpus, called the training set, and reserving the remainder as a testing set; (3) Implement the results of this study in a text generation system; (4) Compare the output of the system with the text found in the entire corpus. This has resulted in the construction of IMAGENE, an instructional text generation system which embodies a model of the forms of expression consistently used by instructional text writers over a broad range of instruction types. The details of IMAGENE's

Verb alternations have been researched extensively in linguistics, but they have not yet received a systematic treatment in natural language generation systems; consequently, generators cannot make informed choices among alternatives. As a step towards overcoming this discrepancy, we review some linguistic work on several prominent alternations, revise and extend it, and suggest a set of rules that allow the series of alternated forms to be produced from a single base form of the verb, the lexical entry. The framework has been implemented in the Moose sentence generator, which can thus choose a particular verb alternation in order to accomplish generation goals such as placing emphasis on the most important element of the sentence.