Adaptive Natural Language Processing

Abstract

A handful of special purpose systems have been successfully deployed to extract prespecified kinds of data from text. The limitation to widespread deployment of such systems is their assumption of a large volume of handcrafted, domain-dependent, and language-dependent knowledge in the form of rules. A new approach is to add automatically trainable probabilistic language models to linguistically based analysis. This offers several potential advantages: (1) Trainability by finding patterns in a large corpus, rather than handcrafting such patterns. (2) Improvability be re-estimating probabilities based on a user marking correct and incorrect output on a test set. (3) More accurate selection among interpretations when more than one is produced. (4) Robustness by finding the most likely partial interpretation when no complete interpretation can be found.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA241336

Entities

People

  • Damaris Ayuso
  • Herbert Gish
  • Robert Bobrow
  • Robert Ingria
  • Sean Boisen

Organizations

  • BBN Technologies

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Computer Science
  • Databases
  • Language
  • Linguistics
  • Machine Learning
  • Markov Models
  • Natural Language Processing
  • Natural Language Understanding
  • Natural Languages
  • Probabilistic Models
  • Probability
  • Test Sets

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation