Generalization and Memory in an Integrated Understanding System.
Abstract
Generalization and memory are part of natural language understanding. As people read stories describing various situations they are able to recall similar episodes form memory and use them as a basis to form generalizations about the way such situations normally occur. This thesis describes an integrated system for language understanding IPP (Integrated Partial Parser), that encompasses the ability to generalize and record information in long-term memory as well as conceptual analysis. IPP is a program that learns about the world by reading stories taken from newspapers and th UPI news wire, adding information from these stories to memory, and making generalizations that describe specific situations. It uses the generalizations that it has made to help in understanding future stories. As it reads stories, IPP adds them to its permanent memory. If it locates similar stories in memory as it does this, then it attempts to make generalizations that describe the similarities among the events. Such generalizations form the basis for organizing events in memory and understanding later stories. IPP also includes a procedure for confirming generalizations as further stories are read. In order to analyze the text that it reads, IPP makes extensive use of top-down, predictive processing. As it processes a story, IPP accesses memory in an attempt to identify generalizations describing stereotypical situations that can provide predictions to be used in understanding. Such use of memory to provide top-down context results in a robust and efficient understanding system. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1980
- Accession Number
- ADA093083
Entities
People
- Michael Lebowitz
Organizations
- Yale University