This paper describes the NLMenu System, a menu-based natural language understanding system. Rather than requiring the user to type his input to the system, input to NLMenu is made by selecting items from a set of dynamically changing menus. Active menus and items are determined by a predictive left-corner parser that accesses a semantic grammar and lexicon. The advantage of this approach is that all inputs to the NLMenu System can be understood thus giving a 0% failure rate. A companion system that can automatically generate interfaces to relational databases is also discussed. relatively straightforward queries that PLANES could understand. Additionally, users did not successfully adapt to the system's limitations after some amount of use.

We show how techniques known from generalized LR parsing can be applied to leftcorner parsing. The esulting parsing algorithm for context-free grammars has some advantages over generalized LR parsing: the sizes and generation times of the parsers are smaller, the produced output is more compact, and the basic parsing technique can more easily be adapted to arbitrary context-free grammars.

Currently several grammatical formalisms converge towards being declarative and towards utilizing context-free phrase-structure grammar as a backbone, e.g. LFG and PATR-II. Typically the processing of these formalisms is organized within a chart-parsing framework. The declarative character of the formalisms makes it important to decide upon an overall optimal control strategy on the part of the processor. In particular, this brings the ruleinvocation strategy into critical focus: to gain maximal processing efficiency, one has to determine the best way of putting the rules to use. The aim of this paper is to provide a survey and a practical comparison of fundamental rule-invocation strategies within context-free chart parsing.

This paper surveys some of these results and discusses their significance for linguistic theory. However, we will avoid entirely the issue of whether one theory is more descrip- tively adequate than another. We will consider context- free, transformational, lexical-functional, generalized phrase structure, tree adjunct, and stratificational grammars) Although this paper focuses on metatheoretic results as arbiters among theories as models of human linguistic capacities, they may have other uses as well. Complexity results could be utilized for making decisions about the implementation of parsers as components of computerbased language-understanding systems. However, as Stanley Peters has pointed out, no one should underestimate "the pleasure to be derived from ferreting out these results! 3 2. Preliminary Definitions We assume that the reader is familiar with the basic defi- nitions of regular, context-free (CF), context-sensitive (CS), recursive, and recursively enumerable (r.e.) languages, as well as with their acceptors (see Hopcroft and Ullman 1979). We will be much concerned with the problem of recognizing whether a string is contained in a given language (the recognition problem) and with that of 1 This research was sponsored in part by the National Science and Engineering Research Council of Canada under Grant A9285. It was made possible in part by a gift from the Systems Development Foundation. An earlier version of this paper appeared in the Proceedings of the 21st ,4nnual Meeting of the ,4ssociation for Computational Linguistics, Cambridge, MA, June 1983

A new approach to bottom.up parsing that extends Augmented Context-Free Grammar to a Process Grammar is formally presented. A Process Grammar (PG ) defines a set of rules suited for bottom-up parsing and conceived as pracesses that are apPlied by a PG Processor. The matching phase is a crucial step for process application, and a parsin 8 structure for efficient matching is also presented. The PG Processor is composed of a process scheduler that allows immediate constituent analysis of structures, and behaves in a non-deterministic fashion. On the other side, the PG offers means for implementing specOc parsing strategies improving the lack of determinism innate in the processor.

this paper we present methods of dealing with the syntactic problems that have arisen in the construction of our LDC system. In particular, we shall describe a gram- matical formalism, based on augmented phrase-structure rules, which allows a parser to make domain-specific decisions by referring to a dictionary and other auxiliary files produced during an initial learning session with the user. We illustrate the workings of our grammatical formalism with examples from the existing LDC grammar, but we note that similarly motivated systems ought also to benefit from our formalisms. We will also include the results of some experiments with our existing grammar as applied to several domains. In addition to showing the theoretical advantage of being able to provide many of the fine-tuning capabilities of so-called semantic grammars within the context of a domain-independent grammar, we demonstrate several practical benefits to our approach. For example, the conciseness of our formalism allows shorter grammars than many previous formalisms would allow, at least for the intended class of retrieval applications. This offers not only added perspicuity but other benefits as well. For instance, we have been able to write simple (almost trivial) LISP routines that pre-process a grammar to construct the files used by the parser to increase efficiency and to perform valuable disambiguations

This paper proposes a series of modifications to the left corner parsing algorithm for context-free grammars. It is argued that the resulting.algorithm is both efficient and flexible and is, therefore, a good choice for the parser used in a natural language interface

The development of a parser for a Norwegian text-to-speech system is reported. The Generalized Left Right (GLR) algorithm [1] is applied, which is a generalization of the well known LR algorithm for parsing computer languages. This paper describes briefly the GLR algorithm, the integration of a probabilistic scoring model, our implementation of the parser in C++, attribute structures, lexical interface, and the application of the parser to part-of-speech (POS) tagging for Norwegian.