Re-Ranking Algorithms for Name Tagging
NEW YORK UNIV NY
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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.
- Information Science