Re-Ranking Algorithms for Name Tagging

Abstract

Integrating information from different stages of an NLP processing pipeline can yield significant error reduction. We demonstrate how re-ranking can improve name tagging in a Chinese information extraction system by incorporating information from relation extraction, event extraction, and coreference. We evaluate three state-of-the-art re-ranking algorithms (MaxEnt-Rank, SVMRank, and p-Norm Push Ranking), and show the benefit of multi-stage re-ranking for cross-sentence and cross-document inference.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2006
Accession Number
ADA456372

Entities

People

  • Cynthia Rudin
  • Heng Ji
  • Ralph David Grishman

Organizations

  • New York University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Computer Science
  • Decoding
  • Identification
  • Language
  • Linear Programming
  • Machine Learning
  • Named Entity Recognition
  • Natural Language Processing
  • New York
  • Probabilistic Models
  • Probability
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks