文章基本信息

标题：Using Bagging and Boosting Techniques for Improving Coreference Resolution
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作者：S. Vemulapalli ; X. Luo ; J.F. Pitrelli 等
期刊名称：Informatica
印刷版ISSN：1514-8327
电子版ISSN：1854-3871
出版年度：2010
卷号：34
期号：1
出版社：The Slovene Society Informatika, Ljubljana
摘要：Classifier combination techniques have been applied to a number of natural language processing problems. This paper explores the use of bagging and boosting as combination approaches for coreference resolu- tion. To the best of our knowledge, this is the first effort that examines and evaluates the applicability of such techniques to coreference resolution. In particular, we (1) outline a scheme for adapting traditional bagging and boosting techniques to address issues, like entity alignment, that are specific to coreference resolution, (2) provide experimental evidence which indicates that the accuracy of the coreference engine can potentially be increased by use of multiple classifiers, without any additional features or training data, and (3) implement and evaluate combination techniques at the mention, entity and document level.
关键词：coreference resolution; information extraction; classifier combination; bagging; boosting; entity detection and;tracking; majority voting