Lexical Selection for Cross-Language Applications: Combining LCS with WordNet

Abstract

This paper describes experiments for testing the power of large-scale resources for lexical selection in machine translation (NIT) and cross-language information retrieval (CLIR). We adopt the view that verbs with similar argument structure share certain meaning components, but that those meaning components are more relevant to argument realization than to idiosyncratic verb meaning. We verify this by demonstrating that verbs with similar argument structure as encoded in Lexical Conceptual Structure (LCS) are rarely synonymous in WordNet. We then use the results of this work to guide our implementation of an algorithm for cross-language selection of lexical items, exploiting the strengths of each resource: LCS for semantic structure and WordNet for semantic content. We use the Parka Knowledge-Based System to encode LCS representations and WordNet synonym sets and we implement our lexical-selection algorithm as Parka-based queries into a knowledge base containing both information types.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1998
Accession Number
ADA458651

Entities

People

  • Bonnie J. Dorr
  • Maria Katsova

Organizations

  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Computers
  • Formal Languages
  • Information Operations
  • Information Retrieval
  • Knowledge Based Systems
  • Language
  • Machine Translation
  • Universities

Fields of Study

  • Computer science

Readers

  • Computational Linguistics

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Machine Translation