Accession Number:

ADA456372

Title:

Re-Ranking Algorithms for Name Tagging

Descriptive Note:

Conference paper

Corporate Author:

NEW YORK UNIV NY

Report Date:

2006-06-01

Pagination or Media Count:

9.0

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.

Subject Categories:

  • Information Science
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE