摘要
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.
出处
《中文信息学报》
CSCD
北大核心
2007年第6期22-28,共7页
Journal of Chinese Information Processing
基金
国家自然科学基金资助项目(60773027)
关键词
计算机应用
中文信息处理
中文共指消解
提及配对共指分类信心
信息抽取
自然语言处理
机器学习
聚类算法
computer application
Chinese information processing
Chinese coreference resolution
confidence in pairwise coreference resolution
information extraction
natural language processing
machine learning
clustering
