Classifier Combination Techniques Applied to Coreference Resolution.

SRWS '09: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium(2009)

引用 8|浏览24
暂无评分
摘要
This paper examines the applicability of classifier combination approaches such as bagging and boosting for coreference resolution. To the best of our knowledge, this is the first effort that utilizes such techniques for coreference resolution. In this paper, we provide experimental evidence which indicates that the accuracy of the coreference engine can potentially be increased by use of bagging and boosting methods, without any additional features or training data. We implement and evaluate combination techniques at the mention, entity and document level, and also address issues like entity alignment, that are specific to coreference resolution.
更多
查看译文
关键词
coreference resolution,coreference engine,classifier combination,combination technique,entity alignment,additional feature,document level,experimental evidence,training data,classifier combination technique
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要