Learning Indices for Conceptual Information Retrieval: An Application of Explanation-Based Learning in Natural Language Processing.

Abstract

Robust natural language processing systems for conceptual information retrieval require a large number of schemata. For both practical and theoretical reasons, a system cannot be initially programmed with all the schemata it requires. It is therefore important for such a system to be able to learn new schemata automatically during its normal operation. This paper describes the ability of GENESIS, a prototype explanation-based learning system for narrative processing, to use the schemata it learns to index and retrieve specific past episodes. An example run is given which illustrates GENESIS's ability to index and retrieve instances of newly learned schemata.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 1987
Accession Number
ADA183472

Entities

People

  • Gerald Dejong
  • Raymond Mooney

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Artificial Intelligence
  • Classification
  • Cognitive Science
  • Computer Languages
  • Computer Science
  • Databases
  • Fungi
  • Illinois
  • Information Retrieval
  • Language
  • Machine Learning
  • Models
  • Natural Language Processing
  • Natural Languages
  • Procurement
  • Universities

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Information Retrieval