Correcting object-related misconceptions (natural language)

Analysis of a corpus of naturally occurring data shows that users conversing with a database or expert system are likely to reveal misconceptions about the objects modelled by the system. Further analysis reveals that the sort of responses given when such misconceptions are encountered depends greatly on the discourse context. This work develops a context-sensitive method for automatically generating responses to object-related misconceptions with the goal of incorporating a correction module in the front-end of a database or expert system. The method is demonstrated through the ROMPER system (Responding to Object-related Misconceptions using PERspective) which is able to generate responses to two classes of object-related misconceptions: misclassifications and misattributions. The transcript analysis reveals a number of specific strategies used by human experts to correct misconceptions, where each different strategy refutes a different kind of support for the misconception. In this work each strategy is paired with a structural specification of the kind of support it refutes. ROMPER uses this specification, and a model of the user, to determine which kind of support is most likely. The corresponding response strategy is then instantiated. The above process is made context sensitive by a proposed addition to standard knowledge-repesentation systems termed object perspective. Object perspective is introduced as a method for augmenting a standard knowledge-representation system to reflect the highlighting affects of previous discourse. It is shown how this resulting highlighting can be used to account for the context-sensitive requirements of the correction process.