Principle-Based Parsing for Machine Translation

Abstract

Many syntactic parsing strategies for machine translation systems are based entirely on context-free grammars. These parsers require an overwhelming number of rules; thus, translation systems using rule-based parsers either have limited linguistic coverage, or they have poor performance due to formidable grammar size. This report shows how a principle-based parser with a co-routine design improves parsing for translation. The parser consists of a skeletal structure-building mechanism that operates in conjunction with a linguistically based constraint module, passing control back and forth until a set of underspecified skeletal phrase-structures is converted into a fully instantiated parse tree. The modularity of the parsing design accommodates linguistic generalization, reduces the grammar size, allows extension to other languages, and is compatible with studies of human language processing. Keywords: Natural language processing, Interlingual translation, Parsing, Subroutines, Principles vs. Rules, Co-routine design, Linguistic constraints.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA199183

Entities

People

  • Bonnie J. Dorr

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Counter IED

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cognitive Science
  • Construction
  • Dictionaries
  • Grammars
  • Information Systems
  • Language
  • Linguistics
  • Lisp Programming Language
  • Machine Translation
  • Military Research
  • Natural Language Processing
  • Natural Languages
  • Rule Based Systems
  • Template Patterns
  • Translations

Readers

  • Artificial Intelligence
  • Computational Linguistics

Technology Areas

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