Description Strategies for Naive and Expert Users

DESCRIPTION STR.ATEGIE.S FOR NAIVE AND EXPERT USERS C~cile L. Paris Department of Computer Science Columbia University New York, NY 10027 Abstract It is widely recognized that a question-answerlng system should be able to tailor its answers to the user. One of the dimensions Mong which thus tailoring can occur is with respect to the level of knowledge of a user about a domain. In particular, responses should be different depending on whether they are addressed to ns/ve or expert users. To understand what those differences should be, we a~alyzed texts from adult and iunior encyclopedias. We found that two different strategies were used in describing complex physical obiects to juniors and adults. We show how these strategies have been implemented on a test database. INTRODUCTION Whether the purpose of a natural language program Ls to ease man-machine interactions [Kaplan 82; Hayes and Reddy 79] or to model human communication ~Lehnert 781, it must take into conslder~tion certain characteristics of the person engaged in the interaction. [n an interaction between people, the goals, beliefs, retentions, knowledge and past experience of the participants will play a role in how they communicate with each other [Cohen and Perrault 791, [Perrault and Allen 80[. Similarly, those characteristics should play a role in the way a computer system interacts with a user. In particular, a questlon-answering program that provides access to a large amount of data to many different users will be most useful if it can tailor its answers to each user. We are interested here in how the level of knowledge (or expertise) of the user a~fects an answer. As an example of this kind of tailoring in a naturally occurring conversation, an explanation of how a car engine works a~med a~ ~ child wdl be different than one ~med ~tt an adult, and an explanation adequate for a music student is probably not quite sufficient for a student in mechanic~l engineering. [n this paper, we study the strategies used tn natural language to describe physical objects to two different types of users: naive and expert. By naive ~nd expert, we refer to how familiar a user m about the domain of the database as opposed to how experienced the user is with the question/aJnswering system. When the database ts complex, it becomes important to vary the level and the kind of details included in the answer in order to provide an answer that caa be best understood by the user. We plan to use this distinction in the question- answering program for RESEARCHER, a system being developed at Columbia University. RESEARCHER reads, remembers, and generalizes from patents abstracts written in English [Lebowitz 83]. The abstracts descrlbe complex physical objects in which spatial and functional relations are important. Thus, we are interested in characterizing spatial strategies that can be used for experts and novices about certmn physical obiects. We give deta41s in the paper of the current implementation of description strategies on a test database of object descriptions. OUR DOMAIN Our goal is …

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