This paper is an introduction to natural language interfaces to databases (Nlidbs). A brief overview of the history of Nlidbs is first given. Some advantages and disadvantages of Nlidbs are then discussed, comparing Nlidbs to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems Nlidbs have to confront follows, for the benefit of readers less familiar with computational linguistics. The discussion then moves on to Nlidb architectures, portability issues, restricted natural language input systems (including menu-based Nlidbs), and Nlidbs with reasoning capabilities. Some less explored areas of Nlidb research are then presented, namely database updates, meta-knowledge questions, temporal questions, and multi-modal Nlidbs. The paper ends with reflections on the current state of the art. 1 Introduction A natural language interface to a database (Nlidb) is a system that allows the user to access information sto...

This report presents a detailed analysis and review of NLP evaluation, in principle and in practice. Part 1 examines evaluation concepts and establishes a framework for NLP system evaluation. This makes use of experience in the related area of information retrieval and the analysis also refers to evaluation in speech processing. Part 2 surveys significant evaluation work done so far, for instance in machine translation, and discusses the particular problems of generic system evaluation. The conclusion is that evaluation strategies and techniques for NLP need much more development, in particular to take proper account of the influence of system tasks and settings. Part 3 develops a general approach to NLP evaluation, aimed at methodologically-sound strategies for test and evaluation motivated by comprehensive performance factor identification. The analysis throughout the report is supported by extensive illustrative examples. This work was carried out under the UK Science and Engineeri...

This paper classifies different types of grammatical deviations and related phenomena at the lexical, sentential and dialogue levels and presents recovery strategies tailored to specific phenomena in the classification. Such strategies constitute a tool chest of computationally tractable methods for coping with extragrammaticality in restricted domain natural language. Some of the strategies have been tested and proven viable in existing parsers

For intelligent interactive systems to communicate with humans in a natural manner, they must have knowledge about the system users. This paper explores the role of user modeling in such systems. It begins with a characterization of what a user model is and how it can be used. The types of information that a user model may be required to keep about a user are then identified and discussed. User models themselves can vary greatly depending on the requirements of the situation and the implementation, so several dimensions along which they can be classified are presented. Since acquiring the knowledge for a user model is a fundamental problem in user modeling, a section is devoted to this topic. Next, the benefits and costs of implementing a user modeling component for a system are weighed in light of several aspects of the interaction requirements that may be imposed by the system. Finally, the current state of research in user modeling is summarized, and future research topics that must be addressed in order to achieve powerful, general user modeling systems are assessed. 1

Abstract not found

this paper, we outline a set of parsing flexibilities that such a system should provide. We go on to describe F!exP, a bottom-up pattern matching parser that we have designed and implemented to provide many of these flexibilities for restricted natural language input to a limited-domain computer system

This paper shows how user modelling can improve the anaphoric utterances generated by a dialogue system. Two kinds of anaphora are examined: contextual ellipsis and the anaphoric use of singular definite noun phrases. In connection with ellipsis generation, anticipation of the way in which the user would be likely to reconstruct a given utterance can help to ensure that the system's utterances are not so brief as to be ambiguous or misleading. When generating noun phrases to characterize specific objects with which the user is not familiar, the system may take into account the existential assumptions, domain-related desires, and referential beliefs ascribed to the partner. These applications of user modelling are illustrated as realized in the dialogue system HAM-ANS, and some possible generalizations and extensions of the strategies described are discussed. Keywords. Natural language generation, anaphora, user modelling, ellipsis, definite descriptions, interest-based answering

This paper presents a pragmatics-based framework for interpreting such utterances. Discourse expectations and focusing heuristics are used to facilitate recognition of an information-seeker's intent in uttering an elliptical fragment. The ellipsis is comprehended by identifying both the aspect of the information-seeker's task-related plan highlighted by the fragment and the conversational discourse goal fulfilled by the utterance. The contribution of this approach is its consideration of pragmatic information, including discourse content and conversational goals, rather than just the precise representation of the preceding utterance

should not be interpreted as representing the official policies, either expressed or implied,

For natural language dialog systems, going beyond domain independence means the attempt to create a core system that can serve as a basis for interfaces to various application classes that differ not only with respect to the domain of discourse but also with respect to dialog type, user type, intended system behavior, and background system. In the design and implementation of HAM-ANS, which is presently operational as an interface to an expert system, a vision system and a data base system, we have shown that going beyond domain independence is possible. HAM-ANS is a large natural language dialog system with both considerable depth and breadth, which accepts typed input in colloquial German and produces typed German responses quickly enough to make it practical for realtime interaction. One goal of this paper is to report on the lessons learned during the realization of the system HAM-ANS. This paper introduces the overall structure of the system's processing units and knowledge sources. In addition we describe some of the innovative features concerning the strategy of semantic interpretation. I.