Re-ranking algorithms for name tagging

Authors:
Heng Ji;Cynthia Rudin;Ralph Grishman
Affiliations:
New York University, New York, N.Y.;New York University, New York, N.Y.;New York University, New York, N.Y.
Venue:
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Year:
2006

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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.