Year: 2006
Type: conference-paper
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.
Authors: Heng Ji, Cynthia Rudin, Ralph Grishman
Institution: New York University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Award: G3190074090
OpenAlex ID: W2078883776