基于分类信心重排序的中文共指消解研究

A Ranking Approach to Chinese Coreference Resolution Based on Pairwise Classification Confidence

共指消解是自然语言处理的核心问题之一。本文针对分步消解中分类器全局信息的不足,依据分类信心对全体提及配对进行排序,优先根据可靠的分类结果对提及进行聚集或分离。实验表明,该算法在多个学习框架下显著地改善了系统的整体性能。

As a typical phenomenon in language, coreference entails vital attention to be resolved in nature language processing. We describe a novel algorithm, which integrates global-evaluated confidence in classification in order to make sure that those pairs which high confidence take high priority in the clustering procedure. The experiments, under supervised learning framework both isolated and joint, show significant gains of the coreference resolution system.