Word-Meaning Selection in Multiprocess Language Understanding Programs

An understander reading or listening to someone speak has to repeatedly solve the problem of word-meaning ambiguity, the selection of the intended meaning of a word from the set of its possible meanings. For example, the problem of pronominal reference can be considered as a choosing of the intended referent from the collection of entities which have already been mentioned or which can be inferred. Human understanders apply rules of syntax, surface semantics, general world knowledge, and various types of contextual knowledge to resolve word-sense or pronominal ambiguity as they process language. We describe a mechanism, called a cooperative word-meaning selector, which allows the computer to use various knowledge sources as it ``understands'' text. The word-meaning selector is part of a conceptual analyzer which forms the natural-language interface for a pair of multiprocess language processing systems. The first, called DSAM (distributable script applier mechanism), reads and summarizes newspaper articles making heavy reference to situational scripts. The second, ACE (academic counseling experiment), is a conversational program which automates certain parts of the academic counseling task. In each of these systems, a variety of knowledge sources, each managed by a distinct ``expert'' process, is brought to bear to enable the word-meaning selector to form the most plausible reading of a sentence containing ambiguous words.