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Journal of Computer Science and Technology 2017, Vol. 32 Issue (4) :749-757    DOI: 10.1007/s11390-017-1756-5
Special Issue on Deep Learning << Previous Articles | Next Articles >>
神经句法融合
Lin-Er Yang1,2,3, Student Member, CCF, Mao-Song Sun1,2,3,4, Senior Member, CCF, Yong Cheng5, Jia-Cheng Zhang1,2,3, Student Member, CCF, Zheng-Hao Liu1,2,3, Student Member, CCF, Huan-Bo Luan1,2,3, Yang Liu1,2,3,4,*, Senior Member, CCF
1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2 State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
3 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
4 Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009, China;
5 Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
Neural Parse Combination
Lin-Er Yang1,2,3, Student Member, CCF, Mao-Song Sun1,2,3,4, Senior Member, CCF, Yong Cheng5, Jia-Cheng Zhang1,2,3, Student Member, CCF, Zheng-Hao Liu1,2,3, Student Member, CCF, Huan-Bo Luan1,2,3, Yang Liu1,2,3,4,*, Senior Member, CCF
1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
2 State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
3 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
4 Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009, China;
5 Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China

摘要
参考文献
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摘要 分析自然语言的句法结构是人工智能中的一项重要任务。由于自然语言的复杂性,不同的句法分析器往往会产生不同的但互补的错误。我们提出了一种基于神经网络的方法来融合来自不同句法分析器的分析结果,从而产生更准确的分析。与传统方法不同,我们的方法直接将线性化的候选分析结果转换为标准答案。在宾州树库上的实验表明,该方法比目前最好的融合方法有较大的提高。
关键词神经网络   句法分析   句法融合     
Abstract: Analyzing the syntactic structure of natural languages by parsing is an important task in artificial intelligence. Due to the complexity of natural languages, individual parsers tend to make different yet complementary errors. We propose a neural network based approach to combine parses from different parsers to yield a more accurate parse than individual ones. Unlike conventional approaches, our method directly transforms linearized candidate parses into the ground-truth parse. Experiments on the Penn English Treebank show that the proposed method improves over a state-of-the-art parser combination approach significantly.
Keywordsneural network   parsing   parse combination     
Received 2016-12-22;
本文基金:

This work is supported by the National Basic Research 973 Program of China under Grant No. 2014CB340501, the Key Program of the National Natural Science Foundation of China under Grant No. 61331013, and the National Natural Science Foundation of China under Grant No. 61522204.

通讯作者: Yang Liu     Email: liuyang2011@tsinghua.edu.cn
About author: Lin-Er Yang is a Ph.D. student at the Department of Computer Science and Technology in Tsinghua University, Beijing. He received his Bachelor's degree in computer science from Beijing Jiaotong University, Beijing, in 2006. His current research interests include representation learning and syntactic parsing.
引用本文:   
Lin-Er Yang, Mao-Song Sun, Yong Cheng, Jia-Cheng Zhang, Zheng-Hao Liu, Huan-Bo L.神经句法融合[J]  Journal of Computer Science and Technology , 2017,V32(4): 749-757
Lin-Er Yang, Mao-Song Sun, Yong Cheng, Jia-Cheng Zhang, Zheng-Hao Liu, Huan-Bo Luan, Yang Liu.Neural Parse Combination[J]  Journal of Computer Science and Technology, 2017,V32(4): 749-757
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