Conceptual Dependency Theory (1969) › Applications: Understanding Stories and Answering Questions

The first major application of Conceptual Dependency was MARGIE (Memory, Analysis, Response Generation, and Inference on English), developed by Schank and his colleagues at Stanford in the early 1970s. MARGIE took English sentences as input, parsed them, converted them to Conceptual Dependency structures, stored these structures in memory, and could then answer questions and generate paraphrases based on what it understood.

MARGIE demonstrated several capabilities that would become central to natural language understanding systems. It could recognize paraphrases: given "John gave Mary a book" and "Mary received a book from John," MARGIE would identify these as expressing the same meaning because they mapped to identical Conceptual Dependency structures. It could answer questions: asked "What did John give to Mary?" it would search its memory for Conceptual Dependency structures with John as ACTOR and Mary as RECIPIENT in an ATRANS, then extract the OBJECT case. It could make inferences: if told "John gave Mary a book," MARGIE could infer that Mary now possesses the book, even though this wasn't explicitly stated.

The system also generated paraphrases. Given a stored Conceptual Dependency structure, it could produce multiple English sentences expressing that meaning. This demonstrated that Conceptual Dependency truly served as an interlingua, a common representation from which various surface forms could be generated, just as it could be used to parse various surface forms into a common representation.

SAM (Script Applier Mechanism), developed at Yale in the mid-1970s, extended Conceptual Dependency to handle stories by incorporating scripts, expectation structures representing stereotypical sequences of events. Given a story like "John went to a restaurant. He ordered a hamburger. He ate it. He left," SAM would use its restaurant script to fill in expected events that weren't explicitly mentioned: John sat down, a waiter brought the hamburger, John paid the bill. These inferences came from understanding the Conceptual Dependency structures of explicitly mentioned events and recognizing which script they activated.

Scripts were essentially networks of Conceptual Dependency structures representing typical event sequences. When SAM encountered "John went to a restaurant," it activated its restaurant script, which included expectations about subsequent events (ordering, eating, paying) and their typical Conceptual Dependency representations. As the story unfolded, SAM matched events to script expectations, filling in missing information and resolving references based on script roles.

PAM (Plan Applier Mechanism), also developed at Yale, addressed stories that didn't fit stereotypical scripts by using plans, goal-directed sequences of actions. When PAM encountered a story about someone trying to achieve a goal, it would infer the plan being followed and use that to understand and predict actions. Plans were represented using Conceptual Dependency structures, with goals linked to the actions that could achieve them.

These systems demonstrated that Conceptual Dependency could support complex inference and discourse understanding. By representing meaning at the right level of abstraction, the theory enabled systems to recognize semantic equivalence, draw commonsense conclusions, and understand extended texts beyond individual sentences.