NDLI: Chinese coreference resolution with ensemble learning

Content Provider	IEEE Xplore Digital Library
Author	Wenbo Pang Xiaozhong Fan
Copyright Year	2009
Description	Author affiliation: School of Computer and Technology, Beijing Institute of Technology, 100081, China (Wenbo Pang; Xiaozhong Fan)
Abstract	Coreference resolution has been shown to be beneficial in many natural language processing (NLP) applications. In the past decades, various strategies were proposed to address this problem. In order to employ these different strategies in a single model, we glue part of them together with an ensemble learning method based on Maximum Entropy (ME). The different coreference resolution strategies combined in this paper include mention-ranking model, mention-entity model and graph-cut-based model, which are high-quality methods today. The experiments on ACE 2004 Chinese data show that the performance of the proposed method is better than those of three basic models, and improve the coreference resolution effectively.
Starting Page	377
Ending Page	380
File Size	903329
Page Count	4
File Format	PDF
ISBN	9781424446063
DOI	10.1109/PACIIA.2009.5406581
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	natural language processing coreference resolution Entropy Application software Data mining Learning systems ensemble learning Aggregates Voting Computer industry Natural language processing Joining processes Computational intelligence
Content Type	Text
Resource Type	Article

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