We present an algorithm for generating strings from logical form encodings that improves upon previous algorithms in that it places fewer restrictions on the class of grammars to which it is applicable. In particular, unlike an Earley deduction generator (Shieber, 1988), it allows use of semantically nonmonotonic grammars, yet unlike topdown methods, it also permits left-recursion. The enabling design feature of the algorithm is its implicit traversal of the analysis tree for the string being generated in a semantic-head-driven fashion.

This paper describes an efficient and robust implementation of a bidirectional, head-driven parser for constraint-based grammars. This parser is developed for the OVIS system: a Dutch spoken dialogue system in which information about public transport can be obtained by telephone. After a Review

In this paper I will discuss the properties of a tactical generation approach that has become popular recently: head-driven bottom-up generation. It is assumed that bidirectional grammars written in some unificationor logic-based formalism define relations between strings and some representation, usually called logical form. The task for a generator is to generate for a given logical form the strings that are related to this logical form by the grammar. In the paper it will be shown that the `early' approaches to this conceivement of the generation problem such as [21], [31] and [7] are not entirely satisfactory for general purposes. Furthermore I will define a simple bottom-up generator, called BUG1 for reference, as prototypical for the head-driven bottom-up approach as defended by for example [30, 5, 23, 24]. I will argue that head-driven bottom-up generation is to be preferred because the order of processing is directed by the input logical form and the information available in le...

this paper is that neither has been implemented." ([Vaughan and McDonald, 1986], page 95). Although Meteer [1990] gives a detail description of the relationship between text structure and revision it is unclear how the proposed model could contribute to the choice problem of paraphrases (see section 5.2). How- ever, from the approach described above and from the system described in [Meteer, 1990] we can draw the following conclusions. Only the generatoFs input is marked. If the generator encounters alternative realizations the revision component is asked to make the decision. However, to be able to do this it needs detailed knowledge about the grammar. Therefore grammatical knowledge has to be duplicated. The linguistic realization component used in [Meteer, 1990] is MUMBLE-86 [McDonald, 1986]. The text structural representation level must completely specify the infor- mation to be expressed by the utterance. The mapping has to ensure that all the necessary linguistic information is present. Mumblers procedural grammar is used only for generation purposes. Therefore it is without reach for the revision model to take into account relevant sources of ambiguities

this paper define the basic concepts of our formalism. Section 4 proves the soundness and completeness of our simplest parser, which is purely bottom-up and excludes rules with empty right-hand sides. Section 5 admits rules with empty right sides, and section 6 adds top-down filtering. Section 7 discusses the implementation and possible exten- sions

This paper describes how to construct a finite-state machine (FSM) approximating a 'unification-based' grammar using a ]eft-corner grammar transform. The approximation is presented as a series of grammar transforms, and is exact for left-linear and rightlinear CFGs, and for trees up to a user-specified depth of center-embedding.

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I describe a head-driven parser for a class of grammars that handle discontinuous constituency by a richer notion of string combination than ordinary concatenation. The parser is a generalization of the left-corner parser and can be used for grammars written in powerful formalisms such as non-concatenative versions of UCG and HPSG.

This report describes research done in the context of a subproject of the HPCN project IMPACT. The IMPACT project is headed by the ING bank and is founded by the organization for High Performance Computing and Networking (HPCN). The aim of the specific subproject, in the context of which this report has been written, is to develop (techniques for) natural language interfaces to information resources, focusing on the use of high-performance computers to achieve acceptable response times. This report is part of the "Parallel Parsing I" research topic. IMPACT-NLI-1997-1 ii Preface IMPACT IMPACT-NLI-1997-1 IMPACT iii Contents Preface i 1 Introduction 1 2 Basics 3