Ralph M. Weischedel

NMR Workshop 1984 Conference Paper

Default Reasoning in Interaction

• Aravind K. Joshi
• Bonnie L. Webber
• Ralph M. Weischedel

AAAI Conference 1983 Conference Paper

Mapping Between Semantic Representations Using Horn Clauses

• Ralph M. Weischedel

Even after an unambiguous semantic interpretation has been computed for a sentence in context, there are at least three reasons that a system may map the semantic representation R into another form S. 1. The terms of R, while reflecting the user view, may require deeper understanding, e.g. may require a version S where metaphors have been analyzed. 2. Transformations of R may be more appropriate for the underlying application system, e.g. S may be a more nearly optimal form. These transformations may not be linguisticly motivated. 3. Some transformations structural context. depend on non- Design considerations may favor factoring the process into two stages, for reasons of understandability or for easier transportability of the components. This paper describes the use of Horn clauses for the three classes of transformations listed above. The transformations are part of a system that converts the English description of a software module into a formal specification, i.e. an abstract data type.

AIJ Journal 1978 Journal Article

An artificial intelligence approach to language instruction

• Ralph M. Weischedel
• Wilfried M. Voge
• Mark James

This paper describes an implemented, prototype system for a sophisticated, intelligent tutor for instruction in a foreign language. The system is an application of artificial intelligence research in natural language, but it implements several ideas that depart from standard approaches to natural language understanding. For instance, the semantic analyzer diagnoses several kinds of comprehension problems and semantic errors that a student might make. Some fine distinctions in meaning are represented to detect misuse of words. Not only is a model of good syntax included in the tutor, but also a model of incorrect forms, rich enough to pinpoint specific syntactic mistakes. Finding the intended interpretation is complicated by the likelihood of student errors. Therefore, perfect syntactic form is not necessary for semantic analysis of the student's input. The problems discussed and solutions presented are closely related to the more general problem of how to respond to a natural language input that surpasses the computer's model of language or of context.