MITRE: Description of the Alembic System Used for MUC-6

Abstract

As with several other veteran MUC participants, MITRE's Alembic system has undergone a major transformation in the past two years. The genesis of this transformation occurred during a dinner conversation at the last MUC conference, MUC-5. At that time, several of us reluctantly admitted that our major impediment towards improved performance was reliance on then-standard linguistic models of syntax. We knew we would need an alternative to traditional linguistic grammars, even to the somewhat non-traditional categorial pseudo-parser we had in place at the time. The problem was, which alternative? The answer came in the form of rule sequences, an approach Eric Brill originally laid out in his work on part-of-speech tagging [5, 7]. Rule sequences now underlie all the major processing steps in Alembic: part-ofspeech tagging, syntactic analysis, inference, and even some of the set-fill processing in the Template Element task (TE). We have found this approach to provide almost an embarrassment of advantages, speed and accuracy being the most externally visible benefits. In addition, most of our rule sequence processors are trainable, typically from small samples. The rules acquired in this way also have the characteristic that they allow one to readily mix hand-crafted and machine-learned elements. We have exploited this opportunity to apply both machine-learned and hand-crafted rules extensively, choosing in some instances to run sequences that were primarily machine-learned, and in other cases to run sequences that were entirely crafted by hand.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1995
Accession Number
ADA633954

Entities

People

  • David Day
  • John Aberdeen
  • John Burger
  • Lynette Hirschman
  • Marc Vilain
  • Patricia Robinson

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Boundaries
  • Coding
  • Corporations
  • Databases
  • Demographic Cohorts
  • Error Analysis
  • Errors
  • Executives
  • Grammars
  • Identification
  • Language
  • Lessons Learned
  • Precision
  • Recognition
  • Test And Evaluation
  • Training

Readers

  • Computational Linguistics
  • Educational Psychology

Technology Areas

  • AI & ML
  • AI & ML - Machine Translation
  • AI & ML - Neural Networks