Re-Ranking Algorithms For Name Tagging
نویسندگان
چکیده
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 stateof-the-art re-ranking algorithms (MaxEntRank, SVMRank, and p-Norm Push Ranking), and show the benefit of multi-stage re-ranking for cross-sentence and crossdocument inference.
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تاریخ انتشار 2006