Language Independent NER using a Unified Model of Internal and Contextual Evidence

Abstract

Abstract This paper investigates the use of a language independent model for named entity recognition based on iterative learning in a co-training fashion, using word-internal and contextual information as independent evidence sources. Its bootstrapping process begins with only seed entities and seed contexts extracted from the provided annotated corpus. F-measure exceeds 77 in Spanish and 72 in Dutch. 1.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA460570

Entities

People

  • David Yarowsky
  • Silviu Cucerzan

Organizations

  • Johns Hopkins University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Boundaries
  • Computer Science
  • Formal Languages
  • Identification
  • Image Processing
  • Information Operations
  • Instructions
  • Language
  • Natural Languages
  • Precision
  • Probability
  • Probability Distributions
  • Recognition
  • Theoretical Computer Science

Readers

  • Computational Linguistics
  • Neural Network Machine Learning.

Technology Areas

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