Tailoring Object Descriptions to a User’s Level of Expertise
Tailoring Object Descriptions to a User’s Level of Expertise
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根据用户的专业水平定制对象描述
DOI：
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发表时间：
1988
期刊：
影响因子：
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通讯作者：
Cécile Paris
中科院分区：
文献类型：
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作者：
Cécile Paris
如果一个问答程序能够为每个用户量身定制答案，那么它就可以提供对大量数据的访问，这将是最有用的。特别是，如果所提供的答案对于用户来说既信息丰富又易于理解，那么用户对话语领域的知识水平是这种定制的一个重要因素。在这项研究中，我们解决了用户的领域知识如何影响答案的问题。通过研究文本，我们发现用户的领域知识水平影响所提供的信息类型，而不仅仅是信息量，正如之前假设的那样。根据用户假设的领域知识，描述可以是面向部件的或面向过程的。因此，用户在某个领域的专业知识水平可以指导系统从知识库中选择适当的事实以包含在答案中。我们提出了两种可在问答程序中使用的不同描述策略，并展示了如何将它们混合起来，以在给定用户的领域知识的情况下包含来自知识库的适当信息。我们在 TAILOR（一个生成设备描述的计算机系统）中实施了这些策略。 TAILOR 使用文本中确定的两种话语策略之一来为新手或专家构建描述。它可以自动合并策略，为介于新手或专家两个极端之间的用户生成各种不同的描述，而不需要先验的用户刻板印象集。
A question answering program providing access to a large amount of data will be most useful if it can tailor its answers to each individual user. In particular, a user's level of knowledge about the domain of discourse is an important factor in this tailoring if the answer provided is to be both informative and understandable to the user. In this research, we address the issue of how the user's domain knowledge can affect an answer. By studying texts, we found that the user's level of domain knowledge affected the kind of information provided and not just the amount of information, as was previously assumed. Depending on the user's assumed domain knowledge, a description can be either parts-oriented or process-oriented. Thus the user's level of expertise in a domain can guide a system in choosing the appropriate facts from the knowledge base to include in an answer. We propose two distinct descriptive strategies that can be used in a question answering program, and show how they can be mixed to include the appropriate information from the knowledge base, given the user's domain knowledge. We have implemented these strategies in TAILOR, a computer system that generates descriptions of devices. TAILOR uses one of the two discourse strategies identified in texts to construct a description for either a novice or an expert. It can merge the strategies automatically to produce a wide range of different descriptions to users who fall between the extremes of novice or expert, without requiring an a priori set of user stereotypes.